limitations and barriers in research

How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

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The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. Study limitations are the constraints placed on the ability to generalize from the results, to further describe applications to practice, and/or related to the utility of findings that are the result of the ways in which you initially chose to design the study or the method used to establish internal and external validity or the result of unanticipated challenges that emerged during the study.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Theofanidis, Dimitrios and Antigoni Fountouki. "Limitations and Delimitations in the Research Process." Perioperative Nursing 7 (September-December 2018): 155-163. .

Importance of...

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and have your grade lowered because you appeared to have ignored them or didn't realize they existed.

Keep in mind that acknowledgment of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgment of a study's limitations also provides you with opportunities to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and discuss how they possibly impacted your results. Note that descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is generally less relevant in qualitative research if explained in the context of the research problem.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but provide cogent reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe a need for future research based on designing a different method for gathering data.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian! In cases when a librarian has confirmed that there is little or no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design ]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to the accuracy of what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency, but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described. Also, include an explanation why being denied or limited access did not prevent you from following through on your study.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, event, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. NOTE :   If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias. For example, if a previous study only used boys to examine how music education supports effective math skills, describe how your research expands the study to include girls.
• Fluency in a language -- if your research focuses , for example, on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic or to speak with these students in their primary language. This deficiency should be acknowledged.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods. Powerpoint Presentation. Regent University of Science and Technology; ter Riet, Gerben et al. “All That Glitters Isn't Gold: A Survey on Acknowledgment of Limitations in Biomedical Studies.” PLOS One 8 (November 2013): 1-6.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings!

After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitations of your study. Inflating the importance of your study's findings could be perceived by your readers as an attempt hide its flaws or encourage a biased interpretation of the results. A small measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated. Or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may very well be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Lewis, George H. and Jonathan F. Lewis. “The Dog in the Night-Time: Negative Evidence in Social Research.” The British Journal of Sociology 31 (December 1980): 544-558.

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgment about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Boddy, Clive Roland. "Sample Size for Qualitative Research." Qualitative Market Research: An International Journal 19 (2016): 426-432; Huberman, A. Michael and Matthew B. Miles. "Data Management and Analysis Methods." In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444; Blaikie, Norman. "Confounding Issues Related to Determining Sample Size in Qualitative Research." International Journal of Social Research Methodology 21 (2018): 635-641; Oppong, Steward Harrison. "The Problem of Sampling in qualitative Research." Asian Journal of Management Sciences and Education 2 (2013): 202-210.

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Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

Table of Contents

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

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How to present limitations in research

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30 January 2024

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Limitations don’t invalidate or diminish your results, but it’s best to acknowledge them. This will enable you to address any questions your study failed to answer because of them.

In this guide, learn how to recognize, present, and overcome limitations in research.

• What is a research limitation?

Research limitations are weaknesses in your research design or execution that may have impacted outcomes and conclusions. Uncovering limitations doesn’t necessarily indicate poor research design—it just means you encountered challenges you couldn’t have anticipated that limited your research efforts.

Does basic research have limitations?

Basic research aims to provide more information about your research topic . It requires the same standard research methodology and data collection efforts as any other research type, and it can also have limitations.

• Common research limitations

Researchers encounter common limitations when embarking on a study. Limitations can occur in relation to the methods you apply or the research process you design. They could also be connected to you as the researcher.

Methodology limitations

Not having access to data or reliable information can impact the methods used to facilitate your research. A lack of data or reliability may limit the parameters of your study area and the extent of your exploration.

Your sample size may also be affected because you won’t have any direction on how big or small it should be and who or what you should include. Having too few participants won’t adequately represent the population or groups of people needed to draw meaningful conclusions.

Research process limitations

The study’s design can impose constraints on the process. For example, as you’re conducting the research, issues may arise that don’t conform to the data collection methodology you developed. You may not realize until well into the process that you should have incorporated more specific questions or comprehensive experiments to generate the data you need to have confidence in your results.

Constraints on resources can also have an impact. Being limited on participants or participation incentives may limit your sample sizes. Insufficient tools, equipment, and materials to conduct a thorough study may also be a factor.

Common researcher limitations

Here are some of the common researcher limitations you may encounter:

Time: some research areas require multi-year longitudinal approaches, but you might not be able to dedicate that much time. Imagine you want to measure how much memory a person loses as they age. This may involve conducting multiple tests on a sample of participants over 20–30 years, which may be impossible.

Bias: researchers can consciously or unconsciously apply bias to their research. Biases can contribute to relying on research sources and methodologies that will only support your beliefs about the research you’re embarking on. You might also omit relevant issues or participants from the scope of your study because of your biases.

Limited access to data : you may need to pay to access specific databases or journals that would be helpful to your research process. You might also need to gain information from certain people or organizations but have limited access to them. These cases require readjusting your process and explaining why your findings are still reliable.

• Why is it important to identify limitations?

Identifying limitations adds credibility to research and provides a deeper understanding of how you arrived at your conclusions.

Constraints may have prevented you from collecting specific data or information you hoped would prove or disprove your hypothesis or provide a more comprehensive understanding of your research topic.

However, identifying the limitations contributing to your conclusions can inspire further research efforts that help gather more substantial information and data.

• Where to put limitations in a research paper

A research paper is broken up into different sections that appear in the following order:

Introduction

Methodology

The discussion portion of your paper explores your findings and puts them in the context of the overall research. Either place research limitations at the beginning of the discussion section before the analysis of your findings or at the end of the section to indicate that further research needs to be pursued.

What not to include in the limitations section

Evidence that doesn’t support your hypothesis is not a limitation, so you shouldn’t include it in the limitation section. Don’t just list limitations and their degree of severity without further explanation.

• How to present limitations

You’ll want to present the limitations of your study in a way that doesn’t diminish the validity of your research and leave the reader wondering if your results and conclusions have been compromised.

Include only the limitations that directly relate to and impact how you addressed your research questions. Following a specific format enables the reader to develop an understanding of the weaknesses within the context of your findings without doubting the quality and integrity of your research.

Identify the limitations specific to your study

You don’t have to identify every possible limitation that might have occurred during your research process. Only identify those that may have influenced the quality of your findings and your ability to answer your research question.

Explain study limitations in detail

This explanation should be the most significant portion of your limitation section.

Link each limitation with an interpretation and appraisal of their impact on the study. You’ll have to evaluate and explain whether the error, method, or validity issues influenced the study’s outcome and how.

Propose a direction for future studies and present alternatives

In this section, suggest how researchers can avoid the pitfalls you experienced during your research process.

If an issue with methodology was a limitation, propose alternate methods that may help with a smoother and more conclusive research project . Discuss the pros and cons of your alternate recommendation.

Describe steps taken to minimize each limitation

You probably took steps to try to address or mitigate limitations when you noticed them throughout the course of your research project. Describe these steps in the limitation section.

• Limitation example

“Approaches like stem cell transplantation and vaccination in AD [Alzheimer’s disease] work on a cellular or molecular level in the laboratory. However, translation into clinical settings will remain a challenge for the next decade.”

The authors are saying that even though these methods showed promise in helping people with memory loss when conducted in the lab (in other words, using animal studies), more studies are needed. These may be controlled clinical trials, for example. 

However, the short life span of stem cells outside the lab and the vaccination’s severe inflammatory side effects are limitations. Researchers won’t be able to conduct clinical trials until these issues are overcome.

• How to overcome limitations in research

You’ve already started on the road to overcoming limitations in research by acknowledging that they exist. However, you need to ensure readers don’t mistake weaknesses for errors within your research design.

To do this, you’ll need to justify and explain your rationale for the methods, research design, and analysis tools you chose and how you noticed they may have presented limitations.

Your readers need to know that even when limitations presented themselves, you followed best practices and the ethical standards of your field. You didn’t violate any rules and regulations during your research process.

You’ll also want to reinforce the validity of your conclusions and results with multiple sources, methods, and perspectives. This prevents readers from assuming your findings were derived from a single or biased source.

• Learning and improving starts with limitations in research

Dealing with limitations with transparency and integrity helps identify areas for future improvements and developments. It’s a learning process, providing valuable insights into how you can improve methodologies, expand sample sizes, or explore alternate approaches to further support the validity of your findings.

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Research Limitations 101 📖

A Plain-Language Explainer (With Practical Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Dr. Eunice Rautenbach | May 2024

Research limitations are one of those things that students tend to avoid digging into, and understandably so. No one likes to critique their own study and point out weaknesses. Nevertheless, being able to understand the limitations of your study – and, just as importantly, the implications thereof – a is a critically important skill.

In this post, we’ll unpack some of the most common research limitations you’re likely to encounter, so that you can approach your project with confidence.

Overview: Research Limitations 101

• What are research limitations ?
• Access – based limitations
• Temporal & financial limitations
• Sample & sampling limitations
• Design limitations
• Researcher limitations
• Key takeaways

What (exactly) are “research limitations”?

At the simplest level, research limitations (also referred to as “the limitations of the study”) are the constraints and challenges that will invariably influence your ability to conduct your study and draw reliable conclusions .

Research limitations are inevitable. Absolutely no study is perfect and limitations are an inherent part of any research design. These limitations can stem from a variety of sources , including access to data, methodological choices, and the more mundane constraints of budget and time. So, there’s no use trying to escape them – what matters is that you can recognise them.

Acknowledging and understanding these limitations is crucial, not just for the integrity of your research, but also for your development as a scholar. That probably sounds a bit rich, but realistically, having a strong understanding of the limitations of any given study helps you handle the inevitable obstacles professionally and transparently, which in turn builds trust with your audience and academic peers.

Simply put, recognising and discussing the limitations of your study demonstrates that you know what you’re doing , and that you’ve considered the results of your project within the context of these limitations. In other words, discussing the limitations is a sign of credibility and strength – not weakness. Contrary to the common misconception, highlighting your limitations (or rather, your study’s limitations) will earn you (rather than cost you) marks.

So, with that foundation laid, let’s have a look at some of the most common research limitations you’re likely to encounter – and how to go about managing them as effectively as possible.

Need a helping hand?

Limitation #1: Access To Information

One of the first hurdles you might encounter is limited access to necessary information. For example, you may have trouble getting access to specific literature or niche data sets. This situation can manifest due to several reasons, including paywalls, copyright and licensing issues or language barriers.

To minimise situations like these, it’s useful to try to leverage your university’s resource pool to the greatest extent possible. In practical terms, this means engaging with your university’s librarian and/or potentially utilising interlibrary loans to get access to restricted resources. If this sounds foreign to you, have a chat with your librarian 🙃

In emerging fields or highly specific study areas, you might find that there’s very little existing research (i.e., literature) on your topic. This scenario, while challenging, also offers a unique opportunity to contribute significantly to your field , as it indicates that there’s a significant research gap .

All of that said, be sure to conduct an exhaustive search using a variety of keywords and Boolean operators before assuming that there’s a lack of literature. Also, remember to snowball your literature base . In other words, scan the reference lists of the handful of papers that are directly relevant and then scan those references for more sources. You can also consider using tools like Litmaps and Connected Papers (see video below).

Limitation #2: Time & Money

Almost every researcher will face time and budget constraints at some point. Naturally, these limitations can affect the depth and breadth of your research – but they don’t need to be a death sentence.

Effective planning is crucial to managing both the temporal and financial aspects of your study. In practical terms, utilising tools like Gantt charts can help you visualise and plan your research timeline realistically, thereby reducing the risk of any nasty surprises. Always take a conservative stance when it comes to timelines, especially if you’re new to academic research. As a rule of thumb, things will generally take twice as long as you expect – so, prepare for the worst-case scenario.

If budget is a concern, you might want to consider exploring small research grants or adjusting the scope of your study so that it fits within a realistic budget. Trimming back might sound unattractive, but keep in mind that a smaller, well-planned study can often be more impactful than a larger, poorly planned project.

If you find yourself in a position where you’ve already run out of cash, don’t panic. There’s usually a pivot opportunity hidden somewhere within your project. Engage with your research advisor or faculty to explore potential solutions – don’t make any major changes without first consulting your institution.

Limitation #3: Sample Size & Composition

As we’ve discussed before , the size and representativeness of your sample are crucial , especially in quantitative research where the robustness of your conclusions often depends on these factors. All too often though, students run into issues achieving a sufficient sample size and composition.

To ensure adequacy in terms of your sample size, it’s important to plan for potential dropouts by oversampling from the outset . In other words, if you aim for a final sample size of 100 participants, aim to recruit 120-140 to account for unexpected challenges. If you still find yourself short on participants, consider whether you could complement your dataset with secondary data or data from an adjacent sample – for example, participants from another city or country. That said, be sure to engage with your research advisor before making any changes to your approach.

A related issue that you may run into is sample composition. In other words, you may have trouble securing a random sample that’s representative of your population of interest. In cases like this, you might again want to look at ways to complement your dataset with other sources, but if that’s not possible, it’s not the end of the world. As with all limitations, you’ll just need to recognise this limitation in your final write-up and be sure to interpret your results accordingly. In other words, don’t claim generalisability of your results if your sample isn’t random.

Limitation #4: Methodological Limitations

As we alluded earlier, every methodological choice comes with its own set of limitations . For example, you can’t claim causality if you’re using a descriptive or correlational research design. Similarly, as we saw in the previous example, you can’t claim generalisability if you’re using a non-random sampling approach.

Making good methodological choices is all about understanding (and accepting) the inherent trade-offs . In the vast majority of cases, you won’t be able to adopt the “perfect” methodology – and that’s okay. What’s important is that you select a methodology that aligns with your research aims and research questions , as well as the practical constraints at play (e.g., time, money, equipment access, etc.). Just as importantly, you must recognise and articulate the limitations of your chosen methods, and justify why they were the most suitable, given your specific context.

Limitation #5: Researcher (In)experience 

A discussion about research limitations would not be complete without mentioning the researcher (that’s you!). Whether we like to admit it or not, researcher inexperience and personal biases can subtly (and sometimes not so subtly) influence the interpretation and presentation of data within a study. This is especially true when it comes to dissertations and theses , as these are most commonly undertaken by first-time (or relatively fresh) researchers.

When it comes to dealing with this specific limitation, it’s important to remember the adage “ We don’t know what we don’t know ”. In other words, recognise and embrace your (relative) ignorance and subjectivity – and interpret your study’s results within that context . Simply put, don’t be overly confident in drawing conclusions from your study – especially when they contradict existing literature.

Cultivating a culture of reflexivity within your research practices can help reduce subjectivity and keep you a bit more “rooted” in the data. In practical terms, this simply means making an effort to become aware of how your perspectives and experiences may have shaped the research process and outcomes.

As with any new endeavour in life, it’s useful to garner as many outsider perspectives as possible. Of course, your university-assigned research advisor will play a large role in this respect, but it’s also a good idea to seek out feedback and critique from other academics. To this end, you might consider approaching other faculty at your institution, joining an online group, or even working with a private coach .

Key Takeaways

Understanding and effectively navigating research limitations is key to conducting credible and reliable academic work. By acknowledging and addressing these limitations upfront, you not only enhance the integrity of your research, but also demonstrate your academic maturity and professionalism.

Whether you’re working on a dissertation, thesis or any other type of formal academic research, remember the five most common research limitations and interpret your data while keeping them in mind.

• Access to Information (literature and data)
• Time and money
• Sample size and composition
• Research design and methodology
• Researcher (in)experience and bias

If you need a hand identifying and mitigating the limitations within your study, check out our 1:1 private coaching service .

Psst… there’s more!

This post is an extract from our bestselling short course, Methodology Bootcamp . If you want to work smart, you don't want to miss this .

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Writing Limitations of Research Study — 4 Reasons Why It Is Important!

It is not unusual for researchers to come across the term limitations of research during their academic paper writing. More often this is interpreted as something terrible. However, when it comes to research study, limitations can help structure the research study better. Therefore, do not underestimate significance of limitations of research study.

Allow us to take you through the context of how to evaluate the limits of your research and conclude an impactful relevance to your results.

Table of Contents

What Are the Limitations of a Research Study?

Every research has its limit and these limitations arise due to restrictions in methodology or research design.  This could impact your entire research or the research paper you wish to publish. Unfortunately, most researchers choose not to discuss their limitations of research fearing it will affect the value of their article in the eyes of readers.

However, it is very important to discuss your study limitations and show it to your target audience (other researchers, journal editors, peer reviewers etc.). It is very important that you provide an explanation of how your research limitations may affect the conclusions and opinions drawn from your research. Moreover, when as an author you state the limitations of research, it shows that you have investigated all the weaknesses of your study and have a deep understanding of the subject. Being honest could impress your readers and mark your study as a sincere effort in research.

Why and Where Should You Include the Research Limitations?

The main goal of your research is to address your research objectives. Conduct experiments, get results and explain those results, and finally justify your research question . It is best to mention the limitations of research in the discussion paragraph of your research article.

At the very beginning of this paragraph, immediately after highlighting the strengths of the research methodology, you should write down your limitations. You can discuss specific points from your research limitations as suggestions for further research in the conclusion of your thesis.

1. Common Limitations of the Researchers

Limitations that are related to the researcher must be mentioned. This will help you gain transparency with your readers. Furthermore, you could provide suggestions on decreasing these limitations in you and your future studies.

2. Limited Access to Information

Your work may involve some institutions and individuals in research, and sometimes you may have problems accessing these institutions. Therefore, you need to redesign and rewrite your work. You must explain your readers the reason for limited access.

3. Limited Time

All researchers are bound by their deadlines when it comes to completing their studies. Sometimes, time constraints can affect your research negatively. However, the best practice is to acknowledge it and mention a requirement for future study to solve the research problem in a better way.

4. Conflict over Biased Views and Personal Issues

Biased views can affect the research. In fact, researchers end up choosing only those results and data that support their main argument, keeping aside the other loose ends of the research.

Types of Limitations of Research

Before beginning your research study, know that there are certain limitations to what you are testing or possible research results. There are different types that researchers may encounter, and they all have unique characteristics, such as:

1. Research Design Limitations

Certain restrictions on your research or available procedures may affect your final results or research outputs. You may have formulated research goals and objectives too broadly. However, this can help you understand how you can narrow down the formulation of research goals and objectives, thereby increasing the focus of your study.

2. Impact Limitations

Even if your research has excellent statistics and a strong design, it can suffer from the influence of the following factors:

• Presence of increasing findings as researched
• Being population specific
• A strong regional focus.

3. Data or statistical limitations

In some cases, it is impossible to collect sufficient data for research or very difficult to get access to the data. This could lead to incomplete conclusion to your study. Moreover, this insufficiency in data could be the outcome of your study design. The unclear, shabby research outline could produce more problems in interpreting your findings.

How to Correctly Structure Your Research Limitations?

There are strict guidelines for narrowing down research questions, wherein you could justify and explain potential weaknesses of your academic paper. You could go through these basic steps to get a well-structured clarity of research limitations:

• Declare that you wish to identify your limitations of research and explain their importance,
• Provide the necessary depth, explain their nature, and justify your study choices.
• Write how you are suggesting that it is possible to overcome them in the future.

In this section, your readers will see that you are aware of the potential weaknesses in your business, understand them and offer effective solutions, and it will positively strengthen your article as you clarify all limitations of research to your target audience.

Know that you cannot be perfect and there is no individual without flaws. You could use the limitations of research as a great opportunity to take on a new challenge and improve the future of research. In a typical academic paper, research limitations may relate to:

1. Formulating your goals and objectives

If you formulate goals and objectives too broadly, your work will have some shortcomings. In this case, specify effective methods or ways to narrow down the formula of goals and aim to increase your level of study focus.

2. Application of your data collection methods in research

If you do not have experience in primary data collection, there is a risk that there will be flaws in the implementation of your methods. It is necessary to accept this, and learn and educate yourself to understand data collection methods.

3. Sample sizes

This depends on the nature of problem you choose. Sample size is of a greater importance in quantitative studies as opposed to qualitative ones. If your sample size is too small, statistical tests cannot identify significant relationships or connections within a given data set.

You could point out that other researchers should base the same study on a larger sample size to get more accurate results.

4. The absence of previous studies in the field you have chosen

Writing a literature review is an important step in any scientific study because it helps researchers determine the scope of current work in the chosen field. It is a major foundation for any researcher who must use them to achieve a set of specific goals or objectives.

However, if you are focused on the most current and evolving research problem or a very narrow research problem, there may be very little prior research on your topic. For example, if you chose to explore the role of Bitcoin as the currency of the future, you may not find tons of scientific papers addressing the research problem as Bitcoins are only a new phenomenon.

It is important that you learn to identify research limitations examples at each step. Whatever field you choose, feel free to add the shortcoming of your work. This is mainly because you do not have many years of experience writing scientific papers or completing complex work. Therefore, the depth and scope of your discussions may be compromised at different levels compared to academics with a lot of expertise. Include specific points from limitations of research. Use them as suggestions for the future.

Have you ever faced a challenge of writing the limitations of research study in your paper? How did you overcome it? What ways did you follow? Were they beneficial? Let us know in the comments below!

Frequently Asked Questions

Setting limitations in our study helps to clarify the outcomes drawn from our research and enhance understanding of the subject. Moreover, it shows that the author has investigated all the weaknesses in the study.

Scope is the range and limitations of a research project which are set to define the boundaries of a project. Limitations are the impacts on the overall study due to the constraints on the research design.

Limitation in research is an impact of a constraint on the research design in the overall study. They are the flaws or weaknesses in the study, which may influence the outcome of the research.

1. Limitations in research can be written as follows: Formulate your goals and objectives 2. Analyze the chosen data collection method and the sample sizes 3. Identify your limitations of research and explain their importance 4. Provide the necessary depth, explain their nature, and justify your study choices 5. Write how you are suggesting that it is possible to overcome them in the future

Excellent article ,,,it has helped me big

This is very helpful information. It has given me an insight on how to go about my study limitations.

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the topic is well covered

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Limitations of the Study – How to Write & Examples

What are the limitations of a study?

The limitations of a study are the elements of methodology or study design that impact the interpretation of your research results. The limitations essentially detail any flaws or shortcomings in your study. Study limitations can exist due to constraints on research design, methodology, materials, etc., and these factors may impact the findings of your study. However, researchers are often reluctant to discuss the limitations of their study in their papers, feeling that bringing up limitations may undermine its research value in the eyes of readers and reviewers.

In spite of the impact it might have (and perhaps because of it) you should clearly acknowledge any limitations in your research paper in order to show readers—whether journal editors, other researchers, or the general public—that you are aware of these limitations and to explain how they affect the conclusions that can be drawn from the research.

In this article, we provide some guidelines for writing about research limitations, show examples of some frequently seen study limitations, and recommend techniques for presenting this information. And after you have finished drafting and have received manuscript editing for your work, you still might want to follow this up with academic editing before submitting your work to your target journal.

Why do I need to include limitations of research in my paper?

Although limitations address the potential weaknesses of a study, writing about them toward the end of your paper actually strengthens your study by identifying any problems before other researchers or reviewers find them.

Furthermore, pointing out study limitations shows that you’ve considered the impact of research weakness thoroughly and have an in-depth understanding of your research topic. Since all studies face limitations, being honest and detailing these limitations will impress researchers and reviewers more than ignoring them.

Where should I put the limitations of the study in my paper?

Some limitations might be evident to researchers before the start of the study, while others might become clear while you are conducting the research. Whether these limitations are anticipated or not, and whether they are due to research design or to methodology, they should be clearly identified and discussed in the discussion section —the final section of your paper. Most journals now require you to include a discussion of potential limitations of your work, and many journals now ask you to place this “limitations section” at the very end of your article. 

Some journals ask you to also discuss the strengths of your work in this section, and some allow you to freely choose where to include that information in your discussion section—make sure to always check the author instructions of your target journal before you finalize a manuscript and submit it for peer review .

Limitations of the Study Examples

There are several reasons why limitations of research might exist. The two main categories of limitations are those that result from the methodology and those that result from issues with the researcher(s).

Common Methodological Limitations of Studies

Limitations of research due to methodological problems can be addressed by clearly and directly identifying the potential problem and suggesting ways in which this could have been addressed—and SHOULD be addressed in future studies. The following are some major potential methodological issues that can impact the conclusions researchers can draw from the research.

Issues with research samples and selection

Sampling errors occur when a probability sampling method is used to select a sample, but that sample does not reflect the general population or appropriate population concerned. This results in limitations of your study known as “sample bias” or “selection bias.”

For example, if you conducted a survey to obtain your research results, your samples (participants) were asked to respond to the survey questions. However, you might have had limited ability to gain access to the appropriate type or geographic scope of participants. In this case, the people who responded to your survey questions may not truly be a random sample.

Insufficient sample size for statistical measurements

When conducting a study, it is important to have a sufficient sample size in order to draw valid conclusions. The larger the sample, the more precise your results will be. If your sample size is too small, it will be difficult to identify significant relationships in the data.

Normally, statistical tests require a larger sample size to ensure that the sample is considered representative of a population and that the statistical result can be generalized to a larger population. It is a good idea to understand how to choose an appropriate sample size before you conduct your research by using scientific calculation tools—in fact, many journals now require such estimation to be included in every manuscript that is sent out for review.

Lack of previous research studies on the topic

Citing and referencing prior research studies constitutes the basis of the literature review for your thesis or study, and these prior studies provide the theoretical foundations for the research question you are investigating. However, depending on the scope of your research topic, prior research studies that are relevant to your thesis might be limited.

When there is very little or no prior research on a specific topic, you may need to develop an entirely new research typology. In this case, discovering a limitation can be considered an important opportunity to identify literature gaps and to present the need for further development in the area of study.

Methods/instruments/techniques used to collect the data

After you complete your analysis of the research findings (in the discussion section), you might realize that the manner in which you have collected the data or the ways in which you have measured variables has limited your ability to conduct a thorough analysis of the results.

For example, you might realize that you should have addressed your survey questions from another viable perspective, or that you were not able to include an important question in the survey. In these cases, you should acknowledge the deficiency or deficiencies by stating a need for future researchers to revise their specific methods for collecting data that includes these missing elements.

Common Limitations of the Researcher(s)

Study limitations that arise from situations relating to the researcher or researchers (whether the direct fault of the individuals or not) should also be addressed and dealt with, and remedies to decrease these limitations—both hypothetically in your study, and practically in future studies—should be proposed.

Limited access to data

If your research involved surveying certain people or organizations, you might have faced the problem of having limited access to these respondents. Due to this limited access, you might need to redesign or restructure your research in a different way. In this case, explain the reasons for limited access and be sure that your finding is still reliable and valid despite this limitation.

Time constraints

Just as students have deadlines to turn in their class papers, academic researchers might also have to meet deadlines for submitting a manuscript to a journal or face other time constraints related to their research (e.g., participants are only available during a certain period; funding runs out; collaborators move to a new institution). The time available to study a research problem and to measure change over time might be constrained by such practical issues. If time constraints negatively impacted your study in any way, acknowledge this impact by mentioning a need for a future study (e.g., a longitudinal study) to answer this research problem.

Conflicts arising from cultural bias and other personal issues

Researchers might hold biased views due to their cultural backgrounds or perspectives of certain phenomena, and this can affect a study’s legitimacy. Also, it is possible that researchers will have biases toward data and results that only support their hypotheses or arguments. In order to avoid these problems, the author(s) of a study should examine whether the way the research problem was stated and the data-gathering process was carried out appropriately.

Steps for Organizing Your Study Limitations Section

When you discuss the limitations of your study, don’t simply list and describe your limitations—explain how these limitations have influenced your research findings. There might be multiple limitations in your study, but you only need to point out and explain those that directly relate to and impact how you address your research questions.

We suggest that you divide your limitations section into three steps: (1) identify the study limitations; (2) explain how they impact your study in detail; and (3) propose a direction for future studies and present alternatives. By following this sequence when discussing your study’s limitations, you will be able to clearly demonstrate your study’s weakness without undermining the quality and integrity of your research.

Step 1. Identify the limitation(s) of the study

• This part should comprise around 10%-20% of your discussion of study limitations.

The first step is to identify the particular limitation(s) that affected your study. There are many possible limitations of research that can affect your study, but you don’t need to write a long review of all possible study limitations. A 200-500 word critique is an appropriate length for a research limitations section. In the beginning of this section, identify what limitations your study has faced and how important these limitations are.

You only need to identify limitations that had the greatest potential impact on: (1) the quality of your findings, and (2) your ability to answer your research question.

Step 2. Explain these study limitations in detail

• This part should comprise around 60-70% of your discussion of limitations.

After identifying your research limitations, it’s time to explain the nature of the limitations and how they potentially impacted your study. For example, when you conduct quantitative research, a lack of probability sampling is an important issue that you should mention. On the other hand, when you conduct qualitative research, the inability to generalize the research findings could be an issue that deserves mention.

Explain the role these limitations played on the results and implications of the research and justify the choice you made in using this “limiting” methodology or other action in your research. Also, make sure that these limitations didn’t undermine the quality of your dissertation .

Step 3. Propose a direction for future studies and present alternatives (optional)

• This part should comprise around 10-20% of your discussion of limitations.

After acknowledging the limitations of the research, you need to discuss some possible ways to overcome these limitations in future studies. One way to do this is to present alternative methodologies and ways to avoid issues with, or “fill in the gaps of” the limitations of this study you have presented.  Discuss both the pros and cons of these alternatives and clearly explain why researchers should choose these approaches.

Make sure you are current on approaches used by prior studies and the impacts they have had on their findings. Cite review articles or scientific bodies that have recommended these approaches and why. This might be evidence in support of the approach you chose, or it might be the reason you consider your choices to be included as limitations. This process can act as a justification for your approach and a defense of your decision to take it while acknowledging the feasibility of other approaches.

P hrases and Tips for Introducing Your Study Limitations in the Discussion Section

The following phrases are frequently used to introduce the limitations of the study:

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”
• “As with the majority of studies, the design of the current study is subject to limitations.”
• “There are two major limitations in this study that could be addressed in future research. First, the study focused on …. Second ….”

For more articles on research writing and the journal submissions and publication process, visit Wordvice’s Academic Resources page.

And be sure to receive professional English editing and proofreading services , including paper editing services , for your journal manuscript before submitting it to journal editors.

Wordvice Resources

Proofreading & Editing Guide

Writing the Results Section for a Research Paper

How to Write a Literature Review

Research Writing Tips: How to Draft a Powerful Discussion Section

How to Captivate Journal Readers with a Strong Introduction

Tips That Will Make Your Abstract a Success!

APA In-Text Citation Guide for Research Writing

Additional Resources

• Diving Deeper into Limitations and Delimitations (PhD student)
• Organizing Your Social Sciences Research Paper: Limitations of the Study (USC Library)
• Research Limitations (Research Methodology)
• How to Present Limitations and Alternatives (UMASS)

Article References

Pearson-Stuttard, J., Kypridemos, C., Collins, B., Mozaffarian, D., Huang, Y., Bandosz, P.,…Micha, R. (2018). Estimating the health and economic effects of the proposed US Food and Drug Administration voluntary sodium reformulation: Microsimulation cost-effectiveness analysis. PLOS. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002551

Xu, W.L, Pedersen, N.L., Keller, L., Kalpouzos, G., Wang, H.X., Graff, C,. Fratiglioni, L. (2015). HHEX_23 AA Genotype Exacerbates Effect of Diabetes on Dementia and Alzheimer Disease: A Population-Based Longitudinal Study. PLOS. Retrieved from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001853

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• v.20(6); Nov-Dec 2015

Challenges in conducting qualitative research in health: A conceptual paper

Hamidreza khankeh.

1 Department of Health in Disaster and Emergencies and Nursing, University of Social Welfare and Rehabilitation, Tehran, Iran and Department of Clinical Sciences and Education, Karolinska Institute, Stockholm, Sweden

Maryam Ranjbar

2 Department of Psychology in Institute of Humanities and Social Studies, and Social Determinants of Health Research Center in University of Social Welfare and Rehabilitation, Tehran, Iran

Davoud Khorasani-Zavareh

3 Social Determinants of Health Research Center, Uremia University of Medical Sciences, Uremia, Iran and Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden

Ali Zargham-Boroujeni

4 Nursing and Midwifery Care Research Center, Faculty of Nursing and Midwifery, Isfahan University of Medical Sciences, Isfahan, Iran

Eva Johansson

5 Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden

Background:

Qualitative research focuses on social world and provides the tools to study health phenomena from the perspective of those experiencing them. Identifying the problem, forming the question, and selecting an appropriate methodology and design are some of the initial challenges that researchers encounter in the early stages of any research project. These problems are particularly common for novices.

Materials and Methods:

This article describes the practical challenges of using qualitative inquiry in the field of health and the challenges of performing an interpretive research based on professional experience as a qualitative researcher and on available literature.

One of the main topics discussed is the nature of qualitative research, its inherent challenges, and how to overcome them. Some of those highlighted here include: identification of the research problem, formation of the research question/aim, and selecting an appropriate methodology and research design, which are the main concerns of qualitative researchers and need to be handled properly. Insights from real-life experiences in conducting qualitative research in health reveal these issues.

Conclusions:

The paper provides personal comments on the experiences of a researcher in conducting pure qualitative research in the field of health. It offers insights into the practical difficulties encountered when performing qualitative studies and offers solutions and alternatives applied by these authors, which may be of use to others.

I NTRODUCTION

Health services and health policy research can be based on qualitative research methods, especially when they deal with a rapid change and develop a more fully integrated theory base and research agenda. However, the field must be with the best traditions and techniques of qualitative methods and should distinguish the essentiality of special training and experience in applying these methods.[ 1 ]

Qualitative research methodologies could help improve our understanding of health-related phenomena. Health knowledge must also include interpretive action to maintain scientific quality when research methods are applied. Qualitative and quantitative strategies should be seen as complementary rather than being thought of as incompatible. Although the procedures of interpreting texts are different from those of statistical analysis, due to their different type of data and questions to be answered, the underlying scientific principles are very much the same.[ 2 ]

While working for more than a decade as qualitative designer, Khankeh faced a lot of challenges in conducting qualitative research in the field of health which occupied the mind of other health researchers. Therefore, this article contributes to the discussion of challenges related to qualitative research in healthcare in the light of personal experiences of a researcher conducting purely qualitative health research.

A M AIN I SSUE FOR THE Q UALITATIVE R ESEARCHER

Qualitative research methods involve systematic collection, organizing, and interpretation of material in textual form derived from talk or observations. They are useful to explore the meanings of social phenomena as experienced by individuals in their natural context. The health community still looks at qualitative research with skepticism and accuses it for the subjective nature and absence of facts. Scientific standards, criteria and checklists do exist and the adequacy of guidelines has been vigorously debated within this cross-disciplinary field.[ 2 ]

Clinical knowledge consists of interpretive action and interaction – factors that involve communication, shared opinions, and experiences. The current quantitative research methods indicate a confined access to clinical knowledge, since they insert only the questions and phenomena that can be controlled, measured, and are countable where it is necessary to investigate, share and contest the tacit knowledge of an experienced practitioner. Qualitative research focuses on the people's social world, and not their disease. It is concerned with increased understanding of the meaning of certain conditions for health professionals and patients, and how their relationships are built in a particular social context.[ 3 ] These kinds of research allow exploration of the social events as experienced by individuals in their natural context. Qualitative inquiry could contribute to a broader understanding of health science [ 4 ] considering the substantial congruence between the core elements of health practice and the principles underpinning qualitative research. The globalization progress augments the necessity of qualitative research.[ 5 ]

Corbin (2008) reported that in the past 10 years, the interest in qualitative methods in general and grounded theory in particular has burgeoned according to a review of the literature and dissertation abstracts.[ 6 ]

A researcher engaged in qualitative research will be confronted with a number of challenges. Identifying the research problem and forming the research question are some of the initial challenges that researchers encounter in the early stages of a qualitative research project. Researchers and students sometimes fail to understand that adopting a qualitative approach is only the first stage in the process of selecting an appropriate research methodology.[ 7 ]

Once the initial research question has been identified, the crucial decision to be made is on the selection of an appropriate method, such as content analysis, ethnography, or grounded theory, and selecting the research design as well. Subsequent arrangements would be on the proper methods of data collection, participants, and the research setting, according to the methodology and the research question.[ 8 ] Qualitative researchers should also handle other important concerns such as data analysis, ethical issues, and rigor methods of results.

In this paper, we are going to discuss important practical challenges of qualitative inquiry in health and the challenges faced by researchers using interpretive research methodologies.

U NDERSTANDING THE R EAL N ATURE OF Q UALITATIVE R ESEARCH AND ITS C HALLENGES

It is important to provide an honest and concise appreciation of the essential characteristics of the qualitative research before discussing the challenges of the interpretive research approach to studies in health.

Virtues of qualitative research

Qualitative research does not promise a clear or direct and orderly method of tackling research problems in health studies. It does not provide researchers with a set of rules to be followed or give them a comforting sense of security and safety backup against possible mistakes on the road to knowledge. This research method depends on the “power of words and images,” but does not offer the assimilated meanings such as numbers and equations; it is rather “an attentive search of meaning and understanding” and an attempt for profound comprehension and awareness of the problems and phenomena. The essentially “diagnostic and exploratory nature” of qualitative research is invaluable in developing conceptualizations in health as an evolving discipline. It tenders the possible tap into the sea of complex interactions in health that can be as follows.

Researchers launch the quest for new theories in health which should acknowledge that “qualitative research is an approach rather than a particular set of techniques, and its appropriateness derives from the nature of the social phenomena to be explored.”[ 9 ] In qualitative research, knowledge derives from the context-specific perspective on the experienced phenomena, interpretations, and explanation of social experiences.

Why qualitative research in the health professions?

Researcher should justify the reason for which he or she selected qualitative research. Qualitative researchers pursue a holistic and exclusive perspective. The approach is helpful in understanding human experiences, which is important for health professionals who focus on caring, communication, and interaction.[ 10 ] Many potential researchers intend to find the answer to the questions about a problem or a major issue in clinical practice or quantitative research can not verify them.

In fact, they choose qualitative research for some significant reasons:

• The emotions, perceptions, and actions of people who suffer from a medical condition can be understood by qualitative research
• The meanings of health professions will only be uncovered through observing the interactions of professionals with clients and interviewing about their experience. This is also applicable to the students destined for the healthcare field
• Qualitative research is individualized; hence, researchers consider the participants as whole human beings, not as a bunch of physical compartments
• Observation and asking people are the only ways to understand the causes of particular behaviors. Therefore, this type of research can develop health or education policies; policies for altering health behavior can only be effective if the behavior's basis is clearly understood.[ 10 , 11 ]

Before adhering to a distinct research methodology, researchers have to exactly understand the nature and character of their inquiries and the knowledge they choose to create. The majority of health researchers face many loopholes in justification. However, all defects and challenges of qualitative research should be realized rather than discarded as a compelling way to knowledge structure. New endeavors in excellent academic achievement and building new tradition of qualitative research in health can be facilitated through acknowledging traps and clarifying the real practical challenges.[ 9 ]

Finally, qualitative research provides investigators with the tools to study the health phenomena from the perspective of those experiencing them. This approach is especially applied in situ ations that have not been previously studied, where major gaps exists in research field, and when there is a need for a new perspective to be identified for the arena of health care intervention.[ 6 ]

Based on corbin and strauss (2008), “ Committed qualitative researchers lean toward qualitative work because they are drawn to the fluid, evolving, and dynamic nature of this approach in contrast to the more rigid and structured format of quantitative methods. Qualitative researchers enjoy serendipity and discovery. It is the endless possibilities to learn more about people that qualitative researchers resonate to. It is not distance that qualitative researchers want between themselves and their participants, but the opportunity to connect with them at a human level (Epistemology). Qualitative researchers have a natural curiosity that leads them to study worlds that interest them and that they otherwise might not have access to. Furthermore, qualitative researchers enjoy playing with words, making order out of seeming disorder, and thinking in terms of complex relationships. For them, doing qualitative research is a challenge that brings the whole self into the process .”

Choosing an approach for health research

Researchers select approaches and methodology based on some scientific logics, not on being easy or interesting. The nature and type of the research question or problem; the researcher's epistemological stance, capabilities, knowledge, skills, and training; and the resources available for the research project are the criteria upon which adopting methodology and procedures depend.[ 6 , 10 ]

Inconsistency between research question and methodology, insufficient methodological knowledge, and lack of attention on philosophical underpinning of qualitative methodology can be mentioned as some important challenges here.

There are several different ways of qualitative research and researchers will have to select between various approaches. The qualitative research is based on the theoretical and philosophical assumptions that researchers try to understand. Then, the research methodology and process should be chosen to be consistent with these basic assumptions and the research question as well.[ 10 ]

Some researchers believe that there is no need to study the methodology and methods before beginning the research. Many researchers neglect to gain this knowledge because they are not aware of the qualitative inquiry complexities which make them go wrong. For instance, lack of information about interview, qualitative data analysis, or sampling is very common.[ 10 ]

My experience shows that lack of knowledge, experience, and skills in a research team to do qualitative research can hinder the formation of original knowledge and improvement in understanding the phenomenon under study. The result of such a study will not be new and interesting, and even the study process will be very mechanical without good interpretation or enough exploration. Sometimes there is an inconsistency between research question, research methodology, and basic philosophical assumptions, and the researchers fail to justify their methods of choice in line with the research question and the ontological and epidemiological assumptions.

Finally, the researcher's intentions, the aims of the research question/inquiry, and the chosen approach are regarded as the most important reasons to select a qualitative research method consistent with them and their underpinning philosophical assumptions as well.[ 6 , 10 ]

Research question and aim

Qualitative research is exciting because it asks questions about people's everyday lives and experiences. A qualitative researcher will have the chance of discovering the “significant truths” in the lives of people. That is a wonderful privilege, but you need to get those questions right if you dig into people's lives and ask about their real experiences. An adequate and explicit research question, or a set of interrelated questions, builds the basis for a good research. But excellent research questions are not easy to write at all. A good research requires a good research question as well because it allows us to identify what we really want to know. However, at the beginning of a project, researchers may be uncertain about what exactly they intend to know, so vague questions can lead to an unfocused project.

Common problems coming up with a research question include:

• Deciding about the research area among a range of issues that are heeded in your field of interest
• Not capable of pointing toward any interesting area or topic sufficient to focus a major piece of work on
• Knowing about the area you want to concentrate on (e.g. emergency), but not a certain topic
• Knowing what area and topic is specifically difficult to articulate a clear question.

Just make sure that you give serious consideration to the chosen area as the basis of your research and that a qualitative project is relevant and possible

Having identified a research area, your next step will be to identify a topic within that interesting area. Research questions should be derived from the literature. The research question can come from the list of “suggestions for future work” at the end of a paper you have found interesting. Moreover, you can search for some verifiable gaps through literature review, or based on your personal or professional experience and expert opinion , which should be studied. Therefore, all the previous studies that have already been conducted in the area are considered as important. In this way, you do not run the risk of asking a research question that has already been addressed and/or answered. Based on my experience, novice researchers have some problems finding the right topics in their field of interest because they do not perform a broad literature review to find the gaps and problems suitable to be investigated. Sometimes their field of interest is different from that of their supervisors or there are no experts to help them in this regard.

Although the topic may retain your interest and you may be committed to undertake such a study, it is important to recognize that some topics of personal relevance may also be deeply significant and difficult to research. Finally you need to make sure that your topic of interest is the one that you can actually study within the project constraints such as time and fund.[ 12 ]

Once you have identified your interesting topic for research (according to a broad literature review, personal and professional experience, and/or expert opinion), you can begin to create a research question.

Forming the research question is one of the initial challenges that researchers encounter in the early stages of a research project. Therefore, it acquires significance by the very fact that it provides brief, but nevertheless, important information on the research topic that allows the reader to decide if the topic is relevant, researchable, and a remarkable issue. Furthermore, the research question in qualitative studies has an additional significance as it determines the manner of conducting the study.

The qualitative research question delineates the procedures that are executed in the study and provides a map to the readers by which they can trail the researcher's intentions and actions in the study. Therefore, special attention is needed on how a qualitative research question will specifically be structured, organized, and formed in the way to quote the necessary information and elements that allow the readers to assess and evaluate the study.

The formation of a qualitative research question acquires a basic conducting role for the study and a fundamental function to develop an audit trail that can empower the readers to judge the value, rigor, and validity of the whole research project. Hence, researchers should not only pay special attention toward developing a significant and relevant question, but also formulate it properly. The qualitative research question must be provided in such a way as to impart, reflect, and conjoin the theoretical and abstract assumptions with the practical and pragmatic means of attaining them.

In plain words, a good qualitative research question implicates particular phrasing, whereas the order of words should make the topic of interest amenable to the qualitative quest.

The researcher has to concentrate on how the content of the research topic is understood when phrasing the qualitative research questions, adhering to the topic with the philosophical/theoretical suggestions and to the structure of the study which requires compounding specific principal elements.

The content of a good qualitative research question takes the form of a declarative rather than an interrogative statement

Also, the content provides a brief focus on the issue to be investigated, but does not define the exact relationship of the variables to make these relationships flexible in emanating from the study according to the qualitative research theory. The qualitative research question incepts necessarily with an active verb like understanding, exploring, interpreting, constructing, explaining, describing, etc., to reflect the paradigm/philosophy underpinning the qualitative study. Consequently, specific nouns that represent the aims of qualitative studies, such as experiences, feelings, views, perspectives, knowledge, etc., should be applied. Finally, the methodology or method should appear in the qualitative research question coherent with them. Meanwhile, the structure of a good qualitative research question will address five of the following six: who, when, where, what, how, and why, and the entire research question should devise the sixth element.[ 13 ]

For instance, “Exploring the experiences of self-immolated women regarding their motives for attempting suicide: A qualitative content analysis study in Kermanshah Iran”

Make sure that your research question is consistent with the approach you are adopting. It is like an easy trap if you decide about the research question before considering the proper way by which you are intending to make assumptions and analyze your data.

My experiences show that novice researchers formulate their research question without considering the approach of their study in a proper way and usually their research questions are very broad, unclear, and vague. Since the intention of their studies is not completely clear at the beginning, they cannot decide about the research approach; also, they have to change their research question and take different directions in the course of study or they will end up without adequate results that can help readers or consumers improve their understanding or solve the problem.

Although a researcher initiates a study with a general question and topic, the interesting aspect of qualitative research is that the questions, which are more specific and can help in further data collection and analysis, arise during the course of the study. Thus, a qualitative research question can be broadly, rather than narrowly, focused in the beginning. Researcher can try to refine and make it more focused later. This is why qualitative research is usually cyclic rather than linear. Qualitative research is cyclic, which means that the research question in this approach immerses gradually into the topic. It means that when you come to know more and more about your topic, your ideas develop about what to focus, either through reading, thinking about what you have read, or in early stages of data analysis. Finally, it is literature review, general reading, and discussion with an expert supervisor that can help you find the right topic. If the background knowledge is poor at the beginning of the study, broad but clear research question can be reasonable. Research question may become more focused or develop in a different direction according to more reading and/or preliminary data analysis. A clear and focused research question is articulated and used to conduct further analysis and any future literature reviews necessary for the final write-up.

However, it is very important to take time to choose a research question, because it can be a very challenging exercise. Actually, the ultimate success of the project depends on selecting a clear and convenient question. The question should be appropriate for the qualitative research and for the specific approach you choose which must be grounded in research. It must ask precisely what you want to find out and be articulated and clear. Knowing this will help you plan your project.[ 12 ]

Choosing the right methodology and research design

Crucial decisions need to be made about an appropriate methodology, such as ethnography or grounded theory, after identifying the initial research question. The main concern of novice researchers is to find the reason and appropriate design to do the research, and proper methodology to answer the question. Researchers ought to figure out about the planning of qualitative research and how to choose the methodology.

Researchers sometimes fail to understand that in the process of selecting an adequate research methodology, adopting a qualitative approach is only the first stage. Students, and sometimes researchers, choose qualitative research because they think it is easier to use than the other methodologies. But this reasoning is fumble since qualitative research is a complex methodology where data collection and analysis can be mostly challenging. Sometimes lack of planning and inadequate attention paid to the properness of the selected approach considering the purpose of research will be problematic.

For new qualitative researchers, it often seems that the researcher should totally concentrate on the dual process of data collection and data analysis. It is very important to consider thorough planning in all stages of the research process, from developing the question to the final write-up of the findings for publication.[ 6 ]

The research design and methodology must be adequate to address the selected topics and the research question. Researchers have to identify, describe, and justify the methodology they chose, besides the strategies and procedures involved. So, it is pivotal to find the proper method for the research question. It should be noticed that some of the details of a qualitative research project cannot be ascertained in advance and may be specified as they arise during the research process.[ 10 ] An important problem for novice researchers is the little acknowledgement of different approaches that address different kinds and levels of questions and take a different stance on the kind of phenomena which is focused upon. More discussion and debates are necessary before selecting and justifying an approach.

The need for consistency and coherence becomes more obvious when we consider the risk of something called “method-slurring.” This is the problem of blurring distinctions between qualitative approaches. Each approach has to demonstrate its consistency to its foundations and will reflect them in data collection, analysis, and knowledge claim.

It may be important to acknowledge the distinctive features by specific approaches such as phenomenology or grounded theory at some levels such as the type of question they are suited to answer, data collection methods they are consistent with, and also the kinds of analysis and presentation of the results that fit within the approach – such as “goodness of fit” or logical staged linking – and can be referred to as “consistency.”

If such consistency occurs, then the whole thing “hangs together” as coherent; that is, the kind of knowledge generated in the results or presentation section doing what is said it would do following the aims of the project. In order to consider these criteria of consistency and coherence in greater detail, we need to look at the distinctive differences between qualitative approaches in the following: the aims of the research approach, its roots in different disciplines and ideologies, the knowledge claims linked to it, and to a lesser extent, the data collection and analysis specific to each approach.[ 11 ]

My experience shows that novice researchers have some problems to justify their methodology of choice and sometimes they experience some degree of methodological slurring. They do not have any clear understanding of the research process in terms of data gathering strategies, data analysis method, and even appropriate sampling plan, which should be indentified based on philosophical and methodological principles.

Finally, besides the above-mentioned problems, regarding research design, there are two common problems encountered especially by students who want to do qualitative study; sometimes researchers and research team try to identify everything, even the sample size, in advance when they design their study because they have a strong background of quantitative research, and this is completely in contrast with the flexible nature and explorative approach of qualitative research. The other problem is the examination committee and the format of proposal of grant sites and funding agencies, which are based on the principles of quantitative study. This rigid format pushes the researchers to try to clarify everything in advance. So, flexibility is regarded as the most important credibility criterion in all kinds of qualitative research and it should be considered when designing the study and following its process.[ 1 ]

C ONCLUSIONS

Qualitative research focuses on social world and provides investigators with the tools to study health phenomena from the perspective of those experiencing them.

Identifying the research problem, forming the research question, and selecting an appropriate methodology and research design are some of the initial challenges that researchers encounter in the early stages of a qualitative research project.

Once the research problem and the initial research question are identified, the crucial decision has to be made in selecting the appropriate methodology. Subsequent arrangements would be on the proper methods of data collection, and choosing the participants and the research setting according to the methodology and the research question. It is highly recommended that the researchers exactly understand the nature and character of their inquiries and the knowledge they choose to create before adhering to a distinct research methodology based on scientific knowledge.

The essence and type of the research question or problem, the researcher's epistemological stance, capabilities, knowledge, skills and training, and the resources available for the research project are the criteria upon which the adopting methodology and procedures depend.

Inconsistency between research question and methodology, insufficient methodological knowledge, and lack of attention to the philosophical underpinning of qualitative methodology are some important challenges.

Lack of knowledge, experience, and skills to do qualitative research can hinder the formation of original knowledge and improvement in understanding the phenomenon under study. The result of such a study will not be new and interesting, and even the study process will be very mechanical without good interpretation or enough exploration. A good research requires a good research question as well because it allows us to identify what we really want to know. However, at the beginning of a project, researchers may be wavering about what they exactly intend to know; so, vague questions can lead to an unfocused project.

Broad literature review, personal and professional experience, and/or expert opinion can be regarded as the main sources to identify interesting research topics and research questions as well. Forming the research question is one of the initial challenges that researchers encounter in the early stages of a research project. Therefore, it acquires significance by the very fact that it provides brief, but nevertheless, important information on the research topic that allows the reader to decide if the topic is relevant, researchable, and a remarkable issue that can help the researcher to determine the manner of conducting the study.

Then crucial decisions need to be made about an appropriate methodology. The main concern of novice researchers is to find the reason and appropriate design to do the research and the proper methodology to answer the question. Researchers first ought to figure out the planning of qualitative research and how to choose the methodology.

It is very important to consider thorough planning in all stages of the research process, from developing the question to final write-up of the findings for publication. It is worth knowing that some of the details of a qualitative research project cannot be ascertained in advance and may be specified as they arise during the research process. For a novice researcher, more discussions and debates are necessary before selecting and justifying an approach.

Method-slurring is another common problem, which means the act of blurring distinctions between qualitative approaches. Each approach has to demonstrate its consistency to its foundations and will reflect them in data collection, analysis, and knowledge claim.

It is not rare to find that researchers and research team try to identify everything, even sample size, in advance when they design their qualitative study because of the strong background they have about the quantitative research. This is completely in contrast with the flexible nature and explorative approach of qualitative research; as these kinds of researches are completely explorative, the mentioned issues – such as sample size – should be clarified in the course of the study.

The other problem is the examination committee and the format of proposal in the grant sites and funding agencies, which is based on the principles of quantitative study. Therefore, flexibility is actually the most important credibility criterion in all qualitative researches that should be considered when a study is designed and the study process is followed.

As the final word, the researcher should make sure that he/she gives serious consideration to the chosen area as the basis of research and that a qualitative project is relevant and possible. Thus, forming the research question in a proper way and selecting appropriate methodology can guarantee original, interesting, and applied knowledge, which at least can increase our understanding about the meaning of certain conditions for professionals and patients and how their relationships are built in a particular social context.

Source of Support: Nil

Conflict of Interest: None declared.

R EFERENCES

Organizing Academic Research Papers: Limitations of the Study

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The limitations of the study are those characteristics of design or methodology that impacted or influenced the application or interpretation of the results of your study. They are the constraints on generalizability and utility of findings that are the result of the ways in which you chose to design the study and/or the method used to establish internal and external validity.

Importance of...

Always acknowledge a study's limitations. It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor and be graded down because you appear to have ignored them.

Keep in mind that acknowledgement of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgement of a study's limitations also provides you with an opportunity to demonstrate to your professor that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitiations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the findings and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Structure: How to Structure the Research Limitations Section of Your Dissertation . Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in your paper.

Here are examples of limitations you may need to describe and to discuss how they possibly impacted your findings. Descriptions of limitations should be stated in the past tense.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but to offer reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe the need for future research.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, consult with a librarian! In cases when a librarian has confirmed that there is a lack of prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design]. Note that this limitation can serve as an important opportunity to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need in future research to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing self-reported data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to take what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data contain several potential sources of bias that should be noted as limitations: (1) selective memory (remembering or not remembering experiences or events that occurred at some point in the past); (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, or documents and, for whatever reason, access is denied or otherwise limited, the reasons for this need to be described.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single research problem, the time available to investigate a research problem and to measure change or stability within a sample is constrained by the due date of your assignment. Be sure to choose a topic that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, or thing is viewed or shown in a consistently inaccurate way. It is usually negative, though one can have a positive bias as well. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places and how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. Note that if you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating bias.
• Fluency in a language -- if your research focuses on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students, for example, and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic. This deficiency should be acknowledged.

Brutus, Stéphane et al. Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations. Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods . Powerpoint Presentation. Regent University of Science and Technology.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as a pilot study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in later studies.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study  is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to reframe your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to  the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't ask a particular question in a survey that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in any future study. A underlying goal of scholarly research is not only to prove what works, but to demonstrate what doesn't work or what needs further clarification.

Brutus, Stéphane et al. Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations. Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. Limitations are not Properly Acknowledged in the Scientific Literature. Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed . January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation . Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings! After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitiations of your study. Inflating of the importance of your study's findings in an attempt hide its flaws is a big turn off to your readers. A measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated, or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Yet Another Writing Tip

A Note about Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgement about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Huberman, A. Michael and Matthew B. Miles. Data Management and Analysis Methods. In Handbook of Qualitative Research. Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444.

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Research Limitations

It is for sure that your research will have some limitations and it is normal. However, it is critically important for you to be striving to minimize the range of scope of limitations throughout the research process.  Also, you need to provide the acknowledgement of your research limitations in conclusions chapter honestly.

It is always better to identify and acknowledge shortcomings of your work, rather than to leave them pointed out to your by your dissertation assessor. While discussing your research limitations, don’t just provide the list and description of shortcomings of your work. It is also important for you to explain how these limitations have impacted your research findings.

Your research may have multiple limitations, but you need to discuss only those limitations that directly relate to your research problems. For example, if conducting a meta-analysis of the secondary data has not been stated as your research objective, no need to mention it as your research limitation.

Research limitations in a typical dissertation may relate to the following points:

1. Formulation of research aims and objectives . You might have formulated research aims and objectives too broadly. You can specify in which ways the formulation of research aims and objectives could be narrowed so that the level of focus of the study could be increased.

2. Implementation of data collection method . Because you do not have an extensive experience in primary data collection (otherwise you would not be reading this book), there is a great chance that the nature of implementation of data collection method is flawed.

3. Sample size. Sample size depends on the nature of the research problem. If sample size is too small, statistical tests would not be able to identify significant relationships within data set. You can state that basing your study in larger sample size could have generated more accurate results. The importance of sample size is greater in quantitative studies compared to qualitative studies.

4. Lack of previous studies in the research area . Literature review is an important part of any research, because it helps to identify the scope of works that have been done so far in research area. Literature review findings are used as the foundation for the researcher to be built upon to achieve her research objectives.

However, there may be little, if any, prior research on your topic if you have focused on the most contemporary and evolving research problem or too narrow research problem. For example, if you have chosen to explore the role of Bitcoins as the future currency, you may not be able to find tons of scholarly paper addressing the research problem, because Bitcoins are only a recent phenomenon.

5. Scope of discussions . You can include this point as a limitation of your research regardless of the choice of the research area. Because (most likely) you don’t have many years of experience of conducing researches and producing academic papers of such a large size individually, the scope and depth of discussions in your paper is compromised in many levels compared to the works of experienced scholars.

You can discuss certain points from your research limitations as the suggestion for further research at conclusions chapter of your dissertation.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

December 18, 2020

We Must Tear Down the Barriers That Impede Scientific Progress

Open science would eliminate article paywalls, data hoarding and siloed lab work

By Michael M. Crow & Greg Tananbaum

Getty Images

We are in the midst of a once-in-a-generation opportunity to remake our approach to science. This moment, in all its difficult uncertainty—COVID-19, economic turmoil and the crescendo of a long overdue national discussion about racial justice—demonstrates why universities, funders and other research stakeholders should move decisively to embrace open science. By adopting what are called “open science” practices, we can align the incentive structures of research production and consumption with our values, and catalyze the scientific progress our society so desperately needs.

The two of us—the president of Arizona State University (ASU), which has topped U.S. News & World Report ’s “Most Innovative Schools” list since the inception of the category in 2016, and the director of the Open Research Funders Group (a collaboration of leading philanthropies that collectively confer more than $10 billion in grants annually)—call on our peers to commit not only in principle, but also in practice, to creating a more efficient, effective and equitable research ecosystem.

Open science, to quote Michael Nielson’s Reinventing Discovery , is “the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process.” That open science is an integral tool in the fight against COVID-19 is indisputable: the importance of access to scientific articles and data to help identify promising vaccines and therapeutics was recognized by publishers and researchers alike early in the pandemic. As a consequence, the research community has worked rapidly to take down the barriers—including article paywalls, data hoarding and siloed lab work— that chronically impede scientific progress.  

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The open dissemination, discussion and testing of COVID-19-related science has quickly taken the place of these outdated norms. Within one month of the first reported case, the virus was rapidly sequenced and openly posted to GenBank, the NIH genetic sequence database. Scores of researchers racing to learn more about COVID-19 shared their early findings as openly accessible preprints. These findings were tested and refined in real-time discussions that were tracked publicly and transparently. Papers that could not withstand replication and reproducibility efforts were quickly and publicly debunked, allowing the scientific community to pursue more promising research avenues. Society and commercial publishers made subscription-controlled coronavirus articles available to all. The protocols and technology behind the Yale School of Public Health’s COVID-19 saliva test have been made available as open source.

Two clear conclusions can be drawn from this rapid alignment. First, the daily workings of science have practical ramifications in all our lives. Scientific norms affect not just researchers working in labs, but also policy makers, doctors, patients, families, and the general public. Second, open science is the form of research dissemination and global collaboration that best reduces vexing limits to knowledge that are exacerbated by COVID-19.  If rapidly and openly sharing research data and papers is critical to understanding and combating coronavirus, doesn’t the same hold true for cancer? Heart disease? Climate change? The scientific community—moving with great speed and clarity of purpose—has clearly signaled that open science is the most efficient way to tackle issues that have a significant and direct effect on the lives of the general public. The unambiguous conclusion is that open is better for science. 

Importantly, open is also better for the economy. For example, around the turn of the century, the massive and massively successful Human Genome Project placed research results in the public domain. This commitment to open science generated nearly $800 billion dollars in economic benefits between 1988 and 2010, a return on investment of $141 for each dollar of the federal government’s investment in the project. More than 310,000 jobs in the U.S. economy were created, directly and indirectly, totaling almost four million job-years of employment as a result of this scientific undertaking. Similarly, we also owe the development of global positioning systems to the real-world implementation of open science principles, a development that produced more than $50 billion in economic benefits . 

Indeed, across a range of sectors from health care to energy, a McKinsey estimate from 2013 put the potential economic value of open data alone in the trillions of dollars annually, equivalent to more than three times the global economic impact of the automotive industry . By leaning into open science practices, we can fuel innovation, job creation and economic growth. As Franklin Roosevelt opined in 1941 , at a similar moment of upheaval and uncertainty, “the enjoyment of the fruits of scientific progress in a wider and constantly rising standard of living” is one of America’s basic expectations.

In addition to being better for science and better for the economy, open is better for society. ASU has found strength in defining success not in whom it excludes, but whom it includes . When knowledge and innovation rest in the hands of the few, we struggle to reach our collective potential. Access to data and published research democratizes information and allows more voices to join the scientific conversation. It removes a layer of insularity in ways both big and small. To take one example at the systemic level, the average library expenditures at Historically Black Colleges and Universities (HBCUs) are significantly less than those of non-HBCU counterparts. This translates in real terms to a racialized inequality of access to the journal articles, books, and other materials upon which future research can be formulated.

At the individual level, the exchange of scientific information often occurs in direct personal interactions. Data that are otherwise proprietary may be shared among close peers and colleagues. Scholars without access to paywalled articles can request copies from the authors, but may be hesitant to do so if they are not part of the same informal networks. By making these materials open for all—to access, replicate, question and build upon—we can contribute to both levelling the playing field and widening the circle of science.

Universities, philanthropies, government agencies and other stakeholders can accelerate the positive effects of open science—in the fight against COVID-19, in our efforts to strengthen the economy, and in our quest for a more just society—by aligning our incentive structures with our values. Practically, this means exercising specific points of leverage—including hiring, funding, tenure and promotion—to ensure that research practices become more open. Many examples are flowering today. Dozens of university departments include language in their job postings along the lines of “This department values transparent, replicable research and open science principles.” This sets the expectation that open practices will be a component of not only the job interview but, for the successful candidate, of the job itself. A wide range of philanthropies are now asking grant applicants to explain how they have historically made their work open, and how, if funded, they will make their outputs open going forward. This provides a powerful incentive (the promise of financial support) for researchers to adhere to open practices. 

Aligning research incentives to reward open science practices may seem daunting, but university and philanthropic leadership can start the process by taking specific, concrete actions that have already been proven effective in practice. While a number of organizations have launched fully actualized open science programs, notably McGill’s Montreal Neurological Institute-Hospital ( The Neuro ) and the Rochester Institute of Technology’s Open@RIT , university presidents and provosts can move their institutions systematically toward open simply by engaging in a structured dialog with their researchers. In this spirit, we call on universities to emulate MIT and launch an open science task force. MIT’s work began with a concise charge from its provost, to “coordinate a renewed Institute-wide discussion of ways in which current policies and practices might be updated or revised to further the Institute’s mission of disseminating the fruits of its research and scholarship as widely as possible.” 

The MIT model is a true collaboration among the administration, chairs and faculty that includes the development and deployment of open science plans tailored to the disciplinary considerations of each department. It is predicated on the acknowledgement that what constitutes open science best practices in, say, anthropology, will differ from what works for zoology. 

Facilitating this bespoke departmental approach are the many emerging norms and policies articulated by professional societies such as the Linguistics Society of America , the Society for the Improvement of Psychological Science and the American Geophysical Union . Ideally, the recommended policies that arise from these task forces will resonate with faculty from both an institutional and a disciplinary perspective. The ultimate goal is to develop "mutually reinforcing vectors," an environment in which researchers hear consistently from a range of influencers at their university, within their discipline, and across potential funding sources that open practices are both warmly encouraged and properly rewarded. 

In support of this "mutually reinforcing vectors" approach, we also call on philanthropies to adopt grantmaking policies that encourage researchers to share their outputs (articles, data, code, materials, etc.) openly and rapidly. In this effort, they can lean on the work of funders ranging from the American Heart Association to the Gates Foundation to the Michael J. Fox Foundation to craft language and workflows that have been field-tested over thousands of grant conferrals. Philanthropies can also draw from policy language templates (developed by the Open Research Funders Group and endorsed by funders including the Sloan Foundation and the Wellcome Trust) to implement a stepwise approach to more closely aligning their incentivization schemes with open science principles.

While open is better for science, the economy, society, it is not magic. It takes concerted, direct effort by key stakeholders to effect change. It also takes a community of practice—sharing successes, roadblocks, and solutions; developing and testing resources that explain the whats, whys and hows of open; and identifying key opportunities to expand the “coalition of the willing.” One such effort is the National Academy of Science, Engineering, and Medicine’s Roundtable on Aligning Incentives for Open Science (in which we both participate). The Roundtable includes direct representation from colleges and universities, philanthropies and government agencies. 

Crucially, the broader network of stakeholders engaging with the Roundtable also includes more than 500 professors, postdocs, librarians, professional society representatives, publishers, funders and other stakeholders. For any university or philanthropy finding itself not yet prepared to take the plunge in the manner we have outlined above, we warmly encourage you to engage with the roundtable to get a better sense of the tangible steps your peers are taking to stimulate open science within their institutions.

There are hurdles to widespread adoption of open science practices, to be sure. Researchers need proper training on data management plans, reuse licensing and other good open science hygiene. Infrastructure must be developed and nurtured to preserve scientific data, curate it and render it actionable. And organizations must overcome their natural entropy, which makes tackling big, cross-cutting initiatives like open science challenging. While these obstacles are nontrivial, they are small in comparison to the scientific, economic, and societal benefits of open. In a moment of great peril, maintaining the status quo will ultimately prove more costly.

Educational resources and simple solutions for your research journey

How to Present the Limitations of a Study in Research?

The limitations of the study convey to the reader how and under which conditions your study results will be evaluated. Scientific research involves investigating research topics, both known and unknown, which inherently includes an element of risk. The risk could arise due to human errors, barriers to data gathering, limited availability of resources, and researcher bias. Researchers are encouraged to discuss the limitations of their research to enhance the process of research, as well as to allow readers to gain an understanding of the study’s framework and value.

Limitations of the research are the constraints placed on the ability to generalize from the results and to further describe applications to practice. It is related to the utility value of the findings based on how you initially chose to design the study, the method used to establish internal and external validity, or the result of unanticipated challenges that emerged during the study. Knowing about these limitations and their impact can explain how the limitations of your study can affect the conclusions and thoughts drawn from your research. 1

Table of Contents

What are the limitations of a study

Researchers are probably cautious to acknowledge what the limitations of the research can be for fear of undermining the validity of the research findings. No research can be faultless or cover all possible conditions. These limitations of your research appear probably due to constraints on methodology or research design and influence the interpretation of your research’s ultimate findings. 2 These are limitations on the generalization and usability of findings that emerge from the design of the research and/or the method employed to ensure validity internally and externally. But such limitations of the study can impact the whole study or research paper. However, most researchers prefer not to discuss the different types of limitations in research for fear of decreasing the value of their paper amongst the reviewers or readers.

Importance of limitations of a study

Writing the limitations of the research papers is often assumed to require lots of effort. However, identifying the limitations of the study can help structure the research better. Therefore, do not underestimate the importance of research study limitations. 3

• Opportunity to make suggestions for further research. Suggestions for future research and avenues for further exploration can be developed based on the limitations of the study.
• Opportunity to demonstrate critical thinking. A key objective of the research process is to discover new knowledge while questioning existing assumptions and exploring what is new in the particular field. Describing the limitation of the research shows that you have critically thought about the research problem, reviewed relevant literature, and correctly assessed the methods chosen for studying the problem.
• Demonstrate Subjective learning process. Writing limitations of the research helps to critically evaluate the impact of the said limitations, assess the strength of the research, and consider alternative explanations or interpretations. Subjective evaluation contributes to a more complex and comprehensive knowledge of the issue under study.

Why should I include limitations of research in my paper

All studies have limitations to some extent. Including limitations of the study in your paper demonstrates the researchers’ comprehensive and holistic understanding of the research process and topic. The major advantages are the following:

• Understand the study conditions and challenges encountered . It establishes a complete and potentially logical depiction of the research. The boundaries of the study can be established, and realistic expectations for the findings can be set. They can also help to clarify what the study is not intended to address.
• Improve the quality and validity of the research findings. Mentioning limitations of the research creates opportunities for the original author and other researchers to undertake future studies to improve the research outcomes.
• Transparency and accountability. Including limitations of the research helps maintain mutual integrity and promote further progress in similar studies.
• Identify potential bias sources.  Identifying the limitations of the study can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.

Where do I need to add the limitations of the study in my paper

The limitations of your research can be stated at the beginning of the discussion section, which allows the reader to comprehend the limitations of the study prior to reading the rest of your findings or at the end of the discussion section as an acknowledgment of the need for further research.

Types of limitations in research

There are different types of limitations in research that researchers may encounter. These are listed below:

• Research Design Limitations : Restrictions on your research or available procedures may affect the research outputs. If the research goals and objectives are too broad, explain how they should be narrowed down to enhance the focus of your study. If there was a selection bias in your sample, explain how this may affect the generalizability of your findings. This can help readers understand the limitations of the study in terms of their impact on the overall validity of your research.
• Impact Limitations : Your study might be limited by a strong regional-, national-, or species-based impact or population- or experimental-specific impact. These inherent limitations on impact affect the extendibility and generalizability of the findings.
• Data or statistical limitations : Data or statistical limitations in research are extremely common in experimental (such as medicine, physics, and chemistry) or field-based (such as ecology and qualitative clinical research) studies. Sometimes, it is either extremely difficult to acquire sufficient data or gain access to the data. These limitations of the research might also be the result of your study’s design and might result in an incomplete conclusion to your research.

Limitations of study examples

All possible limitations of the study cannot be included in the discussion section of the research paper or dissertation. It will vary greatly depending on the type and nature of the study. These include types of research limitations that are related to methodology and the research process and that of the researcher as well that you need to describe and discuss how they possibly impacted your results.

Common methodological limitations of the study

Limitations of research due to methodological problems are addressed by identifying the potential problem and suggesting ways in which this should have been addressed. Some potential methodological limitations of the study are as follows. 1

• Sample size: The sample size 4 is dictated by the type of research problem investigated. If the sample size is too small, finding a significant relationship from the data will be difficult, as statistical tests require a large sample size to ensure a representative population distribution and generalize the study findings.
• Lack of available/reliable data: A lack of available/reliable data will limit the scope of your analysis and the size of your sample or present obstacles in finding a trend or meaningful relationship. So, when writing about the limitations of the study, give convincing reasons why you feel data is absent or untrustworthy and highlight the necessity for a future study focused on developing a new data-gathering strategy.
• Lack of prior research studies: Citing prior research studies is required to help understand the research problem being investigated. If there is little or no prior research, an exploratory rather than an explanatory research design will be required. Also, discovering the limitations of the study presents an opportunity to identify gaps in the literature and describe the need for additional study.
• Measure used to collect the data: Sometimes, the data gathered will be insufficient to conduct a thorough analysis of the results. A limitation of the study example, for instance, is identifying in retrospect that a specific question could have helped address a particular issue that emerged during data analysis. You can acknowledge the limitation of the research by stating the need to revise the specific method for gathering data in the future.
• Self-reported data: Self-reported data cannot be independently verified and can contain several potential bias sources, such as selective memory, attribution, and exaggeration. These biases become apparent if they are incongruent with data from other sources.

General limitations of researchers

Limitations related to the researcher can also influence the study outcomes. These should be addressed, and related remedies should be proposed.

• Limited access to data : If your study requires access to people, organizations, data, or documents whose access is denied or limited, the reasons need to be described. An additional explanation stating why this limitation of research did not prevent you from following through on your study is also needed.
• Time constraints : Researchers might also face challenges in meeting research deadlines due to a lack of timely participant availability or funds, among others. The impacts of time constraints must be acknowledged by mentioning the need for a future study addressing this research problem.
• Conflicts due to biased views and personal issues : Differences in culture or personal views can contribute to researcher bias, as they focus only on the results and data that support their main arguments. To avoid this, pay attention to the problem statement and data gathering.

Steps for structuring the limitations section

Limitations are an inherent part of any research study. Issues may vary, ranging from sampling and literature review to methodology and bias. However, there is a structure for identifying these elements, discussing them, and offering insight or alternatives on how the limitations of the study can be mitigated. This enhances the process of the research and helps readers gain a comprehensive understanding of a study’s conditions.

• Identify the research constraints : Identify those limitations having the greatest impact on the quality of the research findings and your ability to effectively answer your research questions and/or hypotheses. These include sample size, selection bias, measurement error, or other issues affecting the validity and reliability of your research.
• Describe their impact on your research : Reflect on the nature of the identified limitations and justify the choices made during the research to identify the impact of the study’s limitations on the research outcomes. Explanations can be offered if needed, but without being defensive or exaggerating them. Provide context for the limitations of your research to understand them in a broader context. Any specific limitations due to real-world considerations need to be pointed out critically rather than justifying them as done by some other author group or groups.
• Mention the opportunity for future investigations : Suggest ways to overcome the limitations of the present study through future research. This can help readers understand how the research fits into the broader context and offer a roadmap for future studies.

Frequently Asked Questions

• Should I mention all the limitations of my study in the research report?

Restrict limitations to what is pertinent to the research question under investigation. The specific limitations you include will depend on the nature of the study, the research question investigated, and the data collected.

• Can the limitations of a study affect its credibility?

Stating the limitations of the research is considered favorable by editors and peer reviewers. Connecting your study’s limitations with future possible research can help increase the focus of unanswered questions in this area. In addition, admitting limitations openly and validating that they do not affect the main findings of the study increases the credibility of your study. However, if you determine that your study is seriously flawed, explain ways to successfully overcome such flaws in a future study. For example, if your study fails to acquire critical data, consider reframing the research question as an exploratory study to lay the groundwork for more complete research in the future.

• How can I mitigate the limitations of my study?

Strategies to minimize limitations of the research should focus on convincing reviewers and readers that the limitations do not affect the conclusions of the study by showing that the methods are appropriate and that the logic is sound. Here are some steps to follow to achieve this:

• Use data that are valid.
• Use methods that are appropriate and sound logic to draw inferences.
• Use adequate statistical methods for drawing inferences from the data that studies with similar limitations have been published before.

Admit limitations openly and, at the same time, show how they do not affect the main conclusions of the study.

• Can the limitations of a study impact its publication chances?

Limitations in your research can arise owing to restrictions in methodology or research design. Although this could impact your chances of publishing your research paper, it is critical to explain your study’s limitations to your intended audience. For example, it can explain how your study constraints may impact the results and views generated from your investigation. It also shows that you have researched the flaws of your study and have a thorough understanding of the subject.

• How can limitations in research be used for future studies?

The limitations of a study give you an opportunity to offer suggestions for further research. Your study’s limitations, including problems experienced during the study and the additional study perspectives developed, are a great opportunity to take on a new challenge and help advance knowledge in a particular field.

References:

• Brutus, S., Aguinis, H., & Wassmer, U. (2013). Self-reported limitations and future directions in scholarly reports: Analysis and recommendations.  Journal of Management ,  39 (1), 48-75.
• Ioannidis, J. P. (2007). Limitations are not properly acknowledged in the scientific literature.  Journal of Clinical Epidemiology ,  60 (4), 324-329.
• Price, J. H., & Murnan, J. (2004). Research limitations and the necessity of reporting them.  American Journal of Health Education ,  35 (2), 66.
• Boddy, C. R. (2016). Sample size for qualitative research.  Qualitative Market Research: An International Journal ,  19 (4), 426-432.

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How to structure the Research Limitations section of your dissertation

There is no "one best way" to structure the Research Limitations section of your dissertation. However, we recommend a structure based on three moves : the announcing , reflecting and forward looking move. The announcing move immediately allows you to identify the limitations of your dissertation and explain how important each of these limitations is. The reflecting move provides greater depth, helping to explain the nature of the limitations and justify the choices that you made during the research process. Finally, the forward looking move enables you to suggest how such limitations could be overcome in future. The collective aim of these three moves is to help you walk the reader through your Research Limitations section in a succinct and structured way. This will make it clear to the reader that you recognise the limitations of your own research, that you understand why such factors are limitations, and can point to ways of combating these limitations if future research was carried out. This article explains what should be included in each of these three moves :

• THE ANNOUNCING MOVE: Identifying limitations and explaining how important they are
• THE REFLECTING MOVE: Explaining the nature of the limitations and justifying the choices you made
• THE FORWARD LOOKING MOVE: Suggesting how such limitations could be overcome in future

THE ANNOUNCING MOVE Identifying limitations, and explaining how important they are

There are many possible limitations that your research may have faced. However, is not necessary for you to discuss all of these limitations in your Research Limitations section. After all, you are not writing a 2000 word critical review of the limitations of your dissertation, just a 200-500 word critique that is only one section long (i.e., the Research Limitations section within your Conclusions chapter). Therefore, in this first announcing move , we would recommend that you identify only those limitations that had the greatest potential impact on: (a) the quality of your findings; and (b) your ability to effectively answer your research questions and/or hypotheses.

We use the word potential impact because we often do not know the degree to which different factors limited our findings or our ability to effectively answer our research questions and/or hypotheses. For example, we know that when adopting a quantitative research design, a failure to use a probability sampling technique significantly limits our ability to make broader generalisations from our results (i.e., our ability to make statistical inferences from our sample to the population being studied). However, the degree to which this reduces the quality of our findings is a matter of debate. Also, whilst the lack of a probability sampling technique when using a quantitative research design is a very obvious example of a research limitation, other limitations are far less clear. Therefore, the key point is to focus on those limitations that you feel had the greatest impact on your findings, as well as your ability to effectively answer your research questions and/or hypotheses.

Overall, the announcing move should be around 10-20% of the total word count of the Research Limitations section.

THE REFLECTING MOVE Explaining the nature of the limitations and justifying the choices you made

Having identified the most important limitations to your dissertation in the announcing move , the reflecting move focuses on explaining the nature of these limitations and justifying the choices that you made during the research process. This part should be around 60-70% of the total word count of the Research Limitations section.

It is important to remember at this stage that all research suffers from limitations, whether it is performed by undergraduate and master's level dissertation students, or seasoned academics. Acknowledging such limitations should not be viewed as a weakness, highlighting to the person marking your work the reasons why you should receive a lower grade. Instead, the reader is more likely to accept that you recognise the limitations of your own research if you write a high quality reflecting move . This is because explaining the limitations of your research and justifying the choices you made during the dissertation process demonstrates the command that you had over your research.

We talk about explaining the nature of the limitations in your dissertation because such limitations are highly research specific. Let's take the example of potential limitations to your sampling strategy. Whilst you may have a number of potential limitations in sampling strategy, let's focus on the lack of probability sampling ; that is, of all the different types of sampling technique that you could have used [see Types of probability sampling and Types of non-probability sampling ], you choose not to use a probability sampling technique (e.g., simple random sampling , systematic random sampling , stratified random sampling ). As mentioned, if you used a quantitative research design in your dissertation, the lack of probability sampling is an important, obvious limitation to your research. This is because it prevents you from making generalisations about the population you are studying (e.g. Facebook usage at a single university of 20,000 students) from the data you have collected (e.g., a survey of 400 students at the same university). Since an important component of quantitative research is such generalisation, this is a clear limitation. However, the lack of a probability sampling technique is not viewed as a limitation if you used a qualitative research design. In qualitative research designs, a non-probability sampling technique is typically selected over a probability sampling technique.

And this is just part of the puzzle?

Even if you used a quantitative research design, but failed to employ a probability sampling technique, there are still many perfectly justifiable reasons why you could have made such a choice. For example, it may have been impossible (or near on impossible) to get a list of the population you were studying (e.g., a list of all the 20,000 students at the single university you were interested in). Since probability sampling is only possible when we have such a list, the lack of such a list or inability to attain such a list is a perfectly justifiable reason for not using a probability sampling technique; even if such a technique is the ideal.

As such, the purpose of all the guides we have written on research limitations is to help you: (a) explain the nature of the limitations in your dissertation; and (b) justify the choices you made.

In helping you to justifying the choices that you made, these articles explain not only when something is, in theory , an obvious limitation, but how, in practice , such a limitation was not necessarily so damaging to the quality of your dissertation. This should significantly strengthen the quality of your Research Limitations section.

THE FORWARD LOOKING MOVE Suggesting how such limitations could be overcome in future

Finally, the forward looking move builds on the reflecting move by suggesting how the limitations you have discuss could be overcome through future research. Whilst a lot could be written in this part of the Research Limitations section, we would recommend that it is only around 10-20% of the total word count for this section.

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Electronic Theses and Dissertations

The applications and implications of a (re)humanizing praxis for early childhood educational contexts.

Kristopher M. Tetzlaff , University of Denver Follow

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Morgridge College of Education, Teaching and Learning Sciences, Curriculum and Instruction

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Rashida Banerjee

Preschool, Pedagogy, Early childhood education, Access, Humanization

Dehumanizing preschool policies and practices and a lack of access to affordable, (re)humanizing early childhood educative experiences threatens to exacerbate the opportunity gap and deleterious preschool-to-prison pipeline. The purpose of this study was threefold; foremost, it aspired to elevate the role and power of critical resource pedagogies in individual and collective liberation. Second, it aimed to identify vital pedagogical and programmatic assets that advance the pursuit of mutual humanization in early childhood educational settings. Third, it sought to discern and address the pedagogical and programmatic limitations and systemic barriers that otherwise impede access to high quality early childhood programming. In essence, this two-phase single case study leveraged a community-based research approach to examine the perceptions of both young children and parents vis-à-vis the humanizing pedagogical practices implemented at their community-based preschool. Whereby, phase-one parent participants completed a research-based family feedback survey and engaged in a focus group predicated on community-identified needs, phase-two preschool student participants partook in a photovoice experience co-created by community partners. Participant testimonials and photographic submissions reveal, in unanimity, that their community-based preschool effectively operationalizes a (re)humanizing praxis. Furthermore, participant narratives elucidate the applications of a humanizing pedagogy and the implications thereof. Specifically, seven critical themes (i.e., connections with nature; creativity, innovation and imagination; relationships and community connections; curiosity; moments of beauty, joy, wonder, and awe; becoming–cultivating a sense of self; and transcending a sense of self) were inferred across the arc of participant testimony and photographic contributions. These results suggest that, when integrated with veracity and intentionality, a (re)humanizing praxis that tends to the spirit of each child has the potential to disrupt self-replicating cycles of oppression inherent to conventional early childhood educational contexts, one classroom at a time.

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Tetzlaff, Kristopher M., "The Applications and Implications of a (Re)Humanizing Praxis for Early Childhood Educational Contexts" (2024). Electronic Theses and Dissertations . 2424. https://digitalcommons.du.edu/etd/2424

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Deciphering Arabic question: a dedicated survey on Arabic question analysis methods, challenges, limitations and future pathways

• Open access
• Published: 13 August 2024
• Volume 57 , article number  251 , ( 2024 )

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You have full access to this open access article

• Mariam Essam 1 ,
• Mohanad A. Deif 1 &
• Rania Elgohary 1  

This survey reviews different research on question analysis, including other comparative studies of question analysis approaches and an evaluation of the questions by different NLP techniques that are used in question interpretation and categorization. Among these key findings noted includes the assessment of deep learning models such as M-BiGRU-CNN and M-TF-IDF, which come with high precision and accuracy when applied with the effectiveness of use in dealing with the complexities involved in a language. Some of the most mature machine learning algorithms, for example, SVM or logistic regression, remain powerful models, especially on the classification task, meaning that the latter continues to be relevant. This study further underlines the applicability of rule-based or hybrid methodologies in certain linguistic situations, and it must be said that custom design solutions are required. We could recommend, on this basis, directing future work towards the integration of these hybrid systems and towards the definition of more general methodologies of evaluation that are in line with the constant evolution of NLP technologies. It revealed that the underlying challenges and barriers in the domain are very complex syntactic and dialectic variations, unavailability of software tools, very critical standardization in Arabic datasets, benchmark creation, handling of translated data, and the integration of Large Language Models (LLMs). The paper discusses the lack of identity and processing of such structures through online systems for comparison. This comprehensive review highlights not only the diversified potential for the capabilities of NLP techniques in refining question analysis but also the potential way of great promises for further enhancements and improvements in this progressive domain.

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1 Introduction

Question analysis is a critical step in the design of intelligent question-answering systems (QASs) in the naturally growing fields of computer science. Along with a rigorous exploration of more intense scrutiny of question analysis, it deals with a systematic exploration of question analysis with reference to Arabic to trace, analyze, compare, and understand how many approaches were developed to deal with this critical field in Arabic in addition to other natural languages. This paper brings this issue into sharper focus, driven by the complex syntactic nature of Arabic questions and the complex dialectal variation of Arabic. The primary motivation behind this work comes from the peculiar linguistic phenomena that can occur in natural language questions, particularly in Arabic.

This is the primary purpose of text analysis and genre classification. Since the module was designed to work based on an existing domain taxonomy, and the definition of the domain coverage has preceded the module's operation, it is necessary to specify its burden regarding the taxonomy of domains. Of course, it is conceivable within this setting that the analyst who performed the linguistic analysis of the text might have a role as a taxonomist: according to a particular well-developed perspective on texts (though we don't want to promote it here), text's author also understands the taxonomy of the text's domain, and this understanding is expressed through the text's internal organization. In such a scenario, the task of linguistic analysis of the text would be to identify the relevant information in the text's wording and organization to generate the taxonomy of its domain. Such domain specification guides subsequent question analysis and classification processes since it notifies the types of questions your system would handle. Although domain specification is essential, other fundamental vital factors should be given equal standing in the classification of questions. Instead of another sophisticated work, its success and improvement on Arabic questions have highlighted a few essential facts about many classes that have an impact on question comprehension and classification of incoming human queries, which we can classify into many classes such as language variability, type of question, type of analysis, type of key-term extraction, type of intended answer, questions form of representation and related goals.

Language Variety: There are many different dialects of Arabic, which makes it unique. These include Modern Standard Arabic (MSA), used in official settings and the media, various regional dialects with unique lexical and syntactical variations, and Classical Arabic, known for its use in historical and religious writings (Loginova et al. 2021 ).

Question Type: The Arabic QAS must handle different types of questions. This includes factoid questions that seek specific and factual responses and non-factoid questions, which are more complex and varied. Non-factoid questions encompass several sub-categories, each defined by fundamental rhetorical relations such as causal, hypothetical, explanation, purpose, interpretation, base, result, and antithesis (Salem et al. 2010 ; Sadek et al. 2012 ). Additionally, there are list-type questions that expect enumerations, count questions that focus on numerical answers, definition questions requiring explanatory responses, Boolean questions that are answered with a 'yes' or 'no,' and hybrid questions that combine various elements (Alwaneen et al. 2022 ; Guadagno et al. 2020 ; Antoniou and Bassiliades 2022 ). Questions can also be classified as Causal, Hypothetical, seeking Confirmation, or Complex queries involving multiple elements (Alkhurayyif and Sait 2023 ).

Levels of Analysis Performed: This involves several approaches, such as morphological analysis, which identifies the minor units of meaning; syntactic analysis, focusing on the arrangement of words; dependency analysis, examining the relationships between words; semantic analysis, interpreting meanings; and pragmatic and Discourse analysis, particularly in non-factoid(Azmi and Alshenaifi 2017 ) and hybrid QASs, analyzing the use of language in context (Antoniou and Bassiliades 2022 ; Biltawi et al. 2021 ).

Key Term Extraction: This entails determining the focus or keywords from a question and is a crucial component in the classification of question analysis for Arabic QASs. This step is necessary to second-guess what the query intends and, therefore, is essential in interpreting the intention of the input, an exacting interface that essentially aims to weed out near-wrongness. Here, too, we encounter the ubiquitous QAS-standardized component essential to the practical management of the subtleties of Arabic morphosyntax: it guarantees the correctness and contextual accuracy of the system's response.

Expected Answer Type: This unit is organized by the system according to well-known taxonomies—for instance, according to Li and Roth's Taxonomy, Bloom's Taxonomy, or modified forms of them. Li and Roth's Taxonomy is divided into coarse and fine categories. Furthermore, learning objectives that map onto the output categories of a QAS have been further categorized according to Bloom's Taxonomy in its original version and modified forms (Li and Roth 2002 , 2006 ; Abduljabbar and Omar 2015 ; Silva et al. 2019 ; Blyth et al. 1966 ; Michael et al. 1957 ).

Different Question Representations: (1) Set-Theoretic Models (Boolean Model): Use the logical operations AND, OR, and NOT to include or exclude terms in texts. Algebraic Models (Vector Space Model): Documents and queries are ranked as vector representations based on similarity scores. (3) Probability models, also referred to as probabilistic relevance models: Based on the frequency of a phrase, ascertain the relevance of a document. (4) Feature-Based Models: A range of document attributes, such as length and keywords, are integrated to assess a query's relevancy. (5) Conceptual Graph-Based Models: Match and represent concepts by turning questions and answers into graphs (Mishra and Jain 2016 ).

Table 1 presents a detailed compilation, description, and summary of these primary factors. This table illustrates their different roles in responding to queries and language situations. Each element plays a crucial role in characterizing a topic's core and area of expertise, considerably influencing how it is processed, interpreted, and answered by users.

Several recent surveys of Arabic QASs have been undertaken (Alwaneen et al. 2022 ; Alkhurayyif and Sait 2023 ; Biltawi et al. 2021 ), and specific limitations within the Question Analysis module of these systems have been identified and selected. These limitations, which are crucial for understanding the challenges faced by Arabic question analysis, are outlined as follows.

Complexity in Handling Arabic Syntax and Semantics: The intricate syntactic and semantic structures of Arabic pose challenges for the question-analysis module, affecting the accurate interpretation of queries.

Subjectivity and Limited Researchers: The survey involved a few researchers, which might have led to subjective decisions regarding the inclusion, exclusion, and quality criteria applied to selected studies. This could have influenced the absence of certain relevant studies.

Lack of Novelty in Criteria/Taxonomies and Indicators: The criteria/taxonomies and the indicators did not show any newness about what had already been described in the literature.

These surveys have brought to light a variety of characteristics, as well as the variety of QAS types, that are considered significant enough to be included in the Question Analysis and Classification module across QASs. However, these surveys did not concentrate on isolated modules or provide insights into the performance metrics of the question analysis. It is also important to note that the prevalent approach in these systems is to assess the entire system's performance collectively rather than evaluating each component on its own (Biltawi et al. 2021 ).

The substantial role played by the Question Analysis and Classification module in ensuring the precise interpretation of queries is a critical determinant of the overall performance of subsequent modules within a QAS (Alwaneen et al. 2022 ). This module's capacity to accurately comprehend questions is foundational to the efficient operation of the system's later stages. However, the primary challenge lies not just in the complexity of the module itself but also in the limited time available to extract a concise and accurate response from a range of available documents. Therefore, the accuracy of information retrieved is of utmost importance (Brini et al. 2009 ). Further research indicates that, despite Arabic's global importance, there are significant challenges in Arabic QAS development. Due to linguistic and cultural differences, methodologies and algorithms initially developed for English or other languages often do not perform as well when applied to Arabic contexts. Holistic systems that can adeptly handle all questions in Arabic are still scarce. Several existing methods are tailored to specific domains, such as medical or Islamic Arabic QAS, limiting their broader applicability.

This survey is twofold: firstly, to evaluate and contrast the effectiveness of various NLP techniques, ranging from cutting-edge deep learning models to traditional machine learning algorithms, and their application in the realm of question analysis. Secondly, this study aims to identify and discuss researchers' challenges and limitations, such as the intricacies of handling complex syntactical structures, the scarcity of advanced processing tools, and the need for comprehensive and standardized datasets.

Through a meticulous review and comparison of academic research works, this survey seeks to illuminate the strengths and weaknesses of current methodologies, thereby guiding future research directions. Incorporating a broad spectrum of studies provides a holistic view of the current state-of-the-art techniques in question analysis, emphasizing the need for adaptable, context-sensitive approaches in the face of dynamic NLP.

The main goal of this work is to advance knowledge strategically in the vast field of NLP. Doing so will create a strong foundation for developing cutting-edge methodologies to improve question analysis and effectively address the corresponding QAS in Arabic. This study's objective is undoubtedly to further the categorization and analysis of questions.

The results are presented in the following sections of this essay. The process for choosing the pertinent publications and the standards that were adhered to during the selection process are covered in Sect.  1 . The databases, search queries, and evaluation criteria are specified to guarantee that the study is as thorough as feasible and genuinely presents a reproducible one. The results of the Arabic question analysis and their categorization were thoroughly covered in Sect.  2 . This section of the paper compares and summarizes these findings to succinctly present the current state of research on this topic. Section  3 examines the surveyed studies' techniques, problems, and limitations. This section evaluates the study on the analysis and categorization of Arabic questions and combines the results to gain insights. Section  4 provides a concise overview of the main discoveries and proposes areas for future investigation, thus concluding this study. This concluding section examines the outcomes and suggests remedies for the identified problems and limitations.

2 Materials and methods

This survey evaluated and compared question analysis and categorization studies. Since it was unclear how many models or papers would be selected for this study, a scoping review methodology was used due to the expected variance in methods and approaches.

2.1 Eligibility criteria

The analysis included papers available in full text, written in English, and published between 2014 and 2023. The focus of the study was on question analysis and categorization. Research utilizing specialized Arabic QAS techniques was emphasized. Articles that considerably departed from the central theme of Arabic QAS question analysis and classification or used irrelevant methodologies to this study were excluded. The objective was to thoroughly assess various methods and tactics in this research field without imposing limitations on the study protocols. This strategy greatly enhanced the opportunity to explore methodologies for analyzing and categorizing Arabic QAS questions.

2.2 Inclusion criteria for publications

Publications were selected for review if they:

is an academic research work published in conferences or journals written and published in the English language,

focused on Arabic question analysis and classification.

2.3 Exclusion criteria for publication

Publications were considered ineligible for inclusion if they:

Are duplicating papers existing in separate libraries,

focused solely on community-based QASs, those concentrating exclusively on information retrieval or answer extraction within QAS,

studies primarily addressing the similarity between questions were excluded from the review, and,

Publications focusing on developing QASs as a whole rather than only on question analysis.

This exclusion criterion was applied to research that did not cover the extensive facets of Arabic question analysis and classification. The intention was to specifically concentrate on studies offering an in-depth understanding of question analysis and classification, thereby omitting those that merely tackled individual, discrete elements of the Arabic QAS workflow.

2.4 Information sources

Databases: SCOPUS, Google Scholar, IEEE, ACM Digital Library.

2.5 Search strategy for all databases

Use of Boolean operators and keywords related to ( ( ( "Arabic Question Answering System" OR "Arabic Question Analysis" OR "Arabic Question Understanding") AND ( "Dataset" OR "Tools" OR "Survey" OR "Resources")) OR ( ( "Question Answering System" OR "Question Analysis" OR "Question Understanding") AND ( "Dataset" OR "Tools" OR "Survey")) OR ( "Question Analysis Methods" OR "Question Understanding Techniques" OR "Arabic NLP" OR "Natural Language Processing Arabic")) AND ( "Natural Language Processing" OR "Text Mining" OR "Information Retrieval" OR "Computational Linguistics"), over the period 2014–2023.

2.6 Article selection process

An initial pool of 2482 papers was identified through database searches. These searches returned 1056 articles from Scopus, 392 from Google Scholar, 183 from IEEE, and 851 from ACM Digital Library. The selection process was undertaken based on the inclusion and exclusion criteria of the abstracts and titles. Titles and abstracts were screened to refine the selection based on relevance, reducing the number to 200 papers. The full article was considered when a decision could not be made on the abstract and title alone. Finally, an in-depth review of these papers will be conducted to select the 15 most pertinent to the research objectives.

2.7 Data charting

Selected articles are evaluated using a checklist based on the PRISMA 2020 guidelines. The extracted data were recorded in a bespoke Excel spreadsheet.

3.1 Article selection

The survey focused on question analysis and classification in Arabic QASs and utilized a scoping review methodology. This review concentrated on English-language publications from the period of 2014–2023. Inclusion criteria were centered around studies specifically related to the methods used in Arabic QAS question analysis and classification, excluding papers that substantially deviated from this core subject or lacked detailed methodology descriptions. The literature search, covering databases like SCOPUS, Google Scholar, IEEE, and the ACM Digital Library, initially identified 2482 papers. Of the 200 papers, 15 were chosen for their direct relevance to the study goals. Titles, abstracts, and entire articles were evaluated for significance and uncertainty. The data of the selected publications was categorized and assessed using PRISMA 2020. The findings were entered into a customized Excel spreadsheet. Figure  1 shows the scoping review's final report compilation in a PRISMA flow diagram.

Flow diagram showing the paper selection process using the PRISMA approach. NA—Not applicable

3.2 Exploring methodologies and performance in Arabic question analysis and classification

These questions in Arabic are then classified and processed using scholars' methods. The research has involved open and closed-domain systems, performance metrics, and distinctive features. The critical analysis not only represents the difference in the approaches but also provides insight into the specifics of the implementation done in Arabic Question Answering Systems (QASs) and how they present the complexity of the Arabic language.

3.2.1 Open domain

Several studies have made notable contributions to open-domain question analysis and understanding, employing advanced NLP techniques to enhance the classification and interpretation of Arabic questions.

Building upon previous research, Al Chalabi et al. ( 2015 ) introduced an advanced approach to question analysis. This is a well-developed question analysis. Working in such an arrangement, this research has incorporated various syntactical NLP techniques. These are part-of-speech (POS) tagging and parsing in linguistic analysis using Stanford parser and NooJ. The system uses models based on features that aid in identifying the expected answer type by interrogative particles in the question. The system was trained with over 200 Arabic questions designed for practical use and testing by Y. Benajiba, obtainable from this website: [website location]. The system had 186 evaluation test questions prepared per Text REtrieval Conference (TREC) and Conference and Labs of the Evaluation Forum (CLEF) guidelines. Therefore, it is a rule-based system with an analytic approach involving regular expression and context-free grammar to classify and understand the questions correctly. In the evaluation, the methodology in which the system performance was tested showed that the system works very effectively. This gives the Precision (P) of 100%; hence, the system responses are all relevant to the asked questions. The Recall (R) was 93%, showing the retrieved system answers' relevance. It indicates that the system has performed at a very high level with the highest accuracy and efficiency in question analysis; hence, it has successfully processed and responded to the broadest variety of Arabic questions effectively using its syntactical NLP techniques and rule-based approach within an open-domain setting.

Consequently, to follow up on this approach, Al Chalabi et al. ( 2015 ) examined the use of query expansion and POS tagging, in addition to lemmatization, as an impression. Another fundamental method to broaden search inquiries to make them more comprehensive and answerable is AWN. The data is taken from the system from its evaluation and contains 150 Arabic questions. However, all the questions were initially hand-crafted by Y Benajiba to form queries for TREC and CLEF. The questions are widespread, and there is not much of a detected object/s in any particular passage. So, the question set is chosen to test the capabilities of a system to address a wide range of questions that it might be able to handle. Thus, the metric we applied for the final system is the ratio of precision, where we used MRR. Before expansion, the system can answer the queries with good relatedness. However, after adding the query expansion, some queries will score high. Before adding the query expansion, the system's MRR is 1.53 (with %0.56 Average Precision and 0.38 Recall). After adding the query expansion, the system's MRR increased to 2.18 (with an average Precision % of 0.65 and recall of 0.52). It is observable that the addition of the query expansion improved the quality of the system's response when dealing with the inquiry, i.e., the Arabic WordNet method.

Ahmed and Babu ( 2016 ) also used Stanford POS Tagger, Parser, and WordNet in a different study that included a variation of NLP techniques, such as tokenization, stop word removal, POS Tagging, Parsing, Named Entity Recognition (NER), and Query Expansion (Ahmed and Babu 2016 ). The harvested focus word query is also completed with key term extraction using such models. It groups the questions based on the type of response it anticipates being asked. This system uses evaluation and testing datasets in the form of translated TREC-10 documents. A support vector machine (SVM) is included in the system to help with question analysis and classification. The system performs a passable job of processing questions and retrieving answers, as evidenced by its accuracy (ACC) of 65%. This then extends the system's NLP approaches and the classification exercise's capability to relate to precisely how well the system can understand and respond to a range of inquiry kinds on an open-domain limit. It also has implications for displaying that percentage of accuracy at that level.

Lahbari et al. ( 2017a ) combined NLP methods to process and categorize inquiries (Lahbari et al. 2017a ) efficiently. Tokenization breaks text into chunks, pattern matching finds structures, and punctuation and diacritical signs are removed. The Alkhalil Analyzer is crucial to these operations. Li and Roth's taxonomy organizes questions by expected answer kinds. For evaluation, the system uses an Arabic-translated TREC-CLEF dataset. This ensures a range of test questions. This system classifies questions methodically using rules. This system performs effectively and can identify errors with 78% accuracy (Acc). The system's question processing is highly accurate, with an error rate of 3.39%. This high accuracy and low error rate demonstrate how well the rule-based and NLP approaches can evaluate and classify Arabic themes.

Lahbari et al. ( 2017a ) categorize questions using cutting-edge methods in the question analysis subsystem (Lahbari et al. 2017a ). Efficiently or click here to type. Base the classification scope on the predicted answer type using Li and Roth's taxonomy, which classifies questions by their expected responses. The system is evaluated using Arabic translations of TREC and CLEF questions. This ensures a range of test questions. The system classifies questions using SVM, decision trees, and Bayesian algorithms. The best results are produced with the SVM classifier. The SVM classifier can identify many Arabic queries with 84% accuracy. The system's excellent accuracy shows its capacity to apply cutting-edge machine-learning methods for exact question analysis and classification, making it a critical QAS resource.

In Bakari et al. ( 2018 ), proposed a way to clarify Arabic NLU queries into distinct classes concerning different NLP strategies. The necessary strategy of NLP in preparing the text for any subsequent analysis includes tokenization and removing non-Arabic words and punctuation, which must be done while cleaning the text. Key term extractor: one of the prime features of the system, which is most important to find the cascaded sequences of question keywords and main asking idea. Therefore, the system handles the first linguist processing of the queries as the parsing and stemming operations are done using Stanford Parser and the Khoja Stemmer for stemming. The system utilizes conceptual graph-based and feature-based representations of questions due to their systematic and comprehensive approach to expressing questions' logical and semantic elements. Li and Roth's Question classification Taxonomy: Li and Roth taxonomize questions into 5 broad question classes, which may be used to determine the scope of categorization needed for the system. The dataset under consideration for this research will comprise 250 unique factual questions designed to assess the system's fitness. Notably, logic inference or categorization techniques form the basis for processing questions. This implies that the system has an accuracy of 74%, therefore adequate for correctly processing and categorizing the queries. This proves the system's capabilities in using the available approaches of NLP models and exact processing and categorizations of Arabic natural L queries.

Another classification challenge was identified by Hamza et al. ( 2020 ) as an Arabic question classification problem (Hamza et al. 2020 ). They handled it using standard NLP techniques, which included cleaning and tokenizing the text of the query, eliminating punctuation, and using the Modified Term Frequency-Inverse Document Frequency (M-TF-IDF) method to exclude non-Arabic terms. As part of the ELMo (embeddings from language models) toolkit, the Arabic model they employed is a pre-trained deep learning framework called ArELMo (Arabic embeddings from language models). The problems are described by algebraic models in the current system implementation, even if it does not offer methods for keyword extraction. The Expected Answer Type concentrates on the AMIT group, ensuring that the anticipated responses are known, and the questions are categorized accordingly. 3173 Arabic questions from the test dataset were annotated in two taxonomies: a revised version of the Li and Roth (Bakari et al. 2018 ) taxonomy and a brand-new taxonomy designed explicitly for Arabic questions (Hamza et al. 2021 ). The substantial dataset supports a different conclusion, thoroughly verifying the questions' categorization procedure to ensure total system coverage. Therefore, the system here uses various machine-learning approaches to classify questions, clearly demonstrating that it is limited by computer sophistication. The system's fundamental performance matrices are: The weighted F1, macro F1, and accuracy (Acc) values fall between 94 and 95% of the Arabic taxonomy. With the updated classification suggested by Li and Roth, the system classifies it with 92% accuracy and weighted F1 and 83% macro F1. The outcome is sufficient to demonstrate the ability of the system to classify Arabic queries using machine learning and natural language processing methods.

As delineated below, Hamza et al. ( 2020 ), Alammary ( 2021 ) devised Yet Another Arabic Question Analysis in their initial venture into question analysis. This includes natural language processing (NLP) methods such as tokenization (decomposing the text to be split into tokens) and cleaning (removing punctuation). Question representation uses algebraic model brackets based on TF-IDF weighting of N-gram modeling. The task is performed using the modified taxonomy of Li and Roth, which includes a taxonomy suggesting a hierarchical schema of types of questions that we observe and expect an answer to. The purpose of this classification is to calculate the Expected Response Type in a system of question-answering. The dataset comprises 1302 Arabic QTAR questions from TREC, CLEF, and Moroccan schoolbooks. These questions were annotated using two distinct taxonomies: a comprehensive assessment. Several machine learning algorithms are employed for Question Type Significance (QST), including SVM, MLP, and XGBoost. The SVM classifier exhibited an impressive accuracy (Acc) of 90%, showcasing the system's adeptness at handling diverse Arabic questions. This achievement lays the groundwork for developing machine learning models and applying systems mining to analyze, index, and classify Arabic questions.

Using an advanced NLP, Alammary et al. expanded question classification (Alammary 2021 ). Question verbs and important interrogative words remain after all unnecessary punctuation and spaces are removed, except non-Arabic characters and numbers. Furthermore, the updated English TFPOS-IDF is improving extraction and representation. The approach isn't considered a significant advancement in phrase extraction techniques. Queries are generated using the illustrated feature and algebraic models (Hamza et al. 2021 ). Utilizing Bloom's taxonomy anticipates the type of response. This assigns a thinking level to each question. This study tests the implemented method using a fresh dataset of 600 Arabic evaluation questions from several academic disciplines. The ability of the system to classify mail requests using a variety of datasets will be thoroughly examined, and machine learning algorithms such as logistic regression, Naive Bayes, SVM, K-NN, and decision trees will be successfully used to categorize mail requests. The system outperforms other systems, such as TF.IDF and TFPOS.IDF is accurate and precise, has a strong recall, and is F1 in its performance—enhancements to TF.IDF's methodology and machine learning implementations will facilitate the advancement of systems like these in processing large volumes of Arabic inquiries and making the proper deductions, as demonstrated by the accurate classification of Arabic questions.

Balla et al. ( 2022 ) utilized the advanced NLP model and approach of the Inquiry Semantization system to help give intelligent answers to the questions that aid in addressing problems in the medical field. This means improvement in question and system semantics with an advanced model that consists of a pre-trained model, including an ArELMo model that consists of contextual word embedding and word representation. It instantaneously formulates the question using algebraic and feature-based models, though none details the keyword extraction. Bloom's Taxonomy is a framework for studying the range of categories. A framework is used to categorize a study according to its cognitive level. Six hundred and ten questions are tested based on the approaches used within the academic discipline in Arabic assessment on various topics. The developed approach uses the following algorithms to classify the questions: CNN, BiGRU, and M-TFIDF. The representative results are planned systems with R of 84.32%, P of 85.84%, and Acc of 84.26%. All of these measurements demonstrate that the Arabic question classification is excellent. Finish this points to the fact that the system can grasp and process highly variant Arabic queries: indeed, high recall, accuracy, and precision.

The terms were expanded by Malkawi et al. ( 2022 ) by creating an automated system for Arabic question analysis (Malkawi et al. 2022 ). Initially, this involved utilizing an NLP system to remove punctuation, Arabic stop words, diacritical signs, and lengthening from the text body. In addition, this approach uses NER and word representation to provide a more thorough comprehension of the text. In combination with NER, NLTK, and the Bag of Words approach, TF-IDF is employed to process the language. Nonetheless, it lacks a mechanism for identifying critical terms for extraction. Employing a custom taxonomy, the collection of 2581 Arabic questions was categorized into four tiers based on the anticipated answer type. Naïve Bayes, SVM, and logistic regression are a few machine learning algorithms that might be used to classify the questions. Based on the scores, the three most accurate ones were Multinomial Naive Bayes (79%), followed by Linear SVM (81%), and Logistic Regression (82%). Thus, it can be said that nearly all machine learning and natural language processing approaches have been appropriately tuned to achieve the necessary objective of identifying and comprehending a large number of Arabic natural language inquiries.

In their subsequent exploration of categorizing Arabic inquiries, Hamza et al. ( 2022 ) highlighted word splitting, context-based word representations, word representations, and the concept of OOV (out-of-vocabulary). These include Word2Vec, BERT, ELMo, and the Word Piece model. Rather than relying on crucial phrase extraction algorithms, the system utilizes these models to classify the queries, expressing them algebraically. The questions are categorized using an organized process based on the kind of response that is anticipated. It consists of 3,503 TREC questions, 800 CLEF questions, and 870 measurable Moroccan schoolbook questions. When conducting a question class, some machine learning approaches to be used are Naive Bayes, SVM, CNN, LSTM along with Vanilla Coarse AraBERT, Coarse concat AraBERT_W2V, and 3OH Coarse AraBERT_ELMo. Arabic_concat has the highest Macro F1, Weighted F1, and Accuracy values for Arabic settings. Under Vanilla Arabic AraBERT, the accuracy is 94.20%, 94.19% weighted F1, and 93.79% macro F1. This further demonstrates the system's accuracy in classifying Arabic inquiries, wherein most NLP and fusion approaches have handled and realized a wide range of Arabic queries. By employing sophisticated natural language processing (NLP) and computational methods to evaluate and interpret Arabic queries, these studies show progress in analyzing and comprehending open-domain questions.

3.2.2 Closed domain

Several closed-domain question analysis and understanding investigations have improved question classification using advanced NLP techniques.

Question subsystem for "higher education" evaluation, as described by Dwivedi and Singh ( 2014 ). The queries are represented by feature-based models incorporating natural language processing (NLP) techniques such as chunking, tokenization, POS tagging, and pattern matching. These models derive crucial terms. Using a comprehensive framework and a modified version of Li and Roth's taxonomy, queries are classified into 61 fine and 9 coarse categories. By utilizing heuristics and pattern matching, the algorithm achieves an accuracy of 87% in classifying pedagogical questions.

Faris et al. ( 2022 ) classified Arabic medical search queries about healthcare (Faris et al. 2022 ). The system represents queries using Word2Vec tokenization, normalization, denoising, and word embedding. Although the system employs probabilistic and algebraic models to illustrate questions, extracting essential terms lacks documentation. With an accuracy of 87.2%, deep learning techniques such as LSTM and BiLSTM classify queries into 15 medical specialities. Accuracy indicates that the system is capable of processing complex Arabic medical queries.

Dardour et al. ( 2022 ) constructed a subsystem for healthcare-oriented question analysis by implementing query expansion, named entity recognition (NER), stemming, stop word deletion, and stemming (Dardour et al. 2022 ). Set-theoretic models categorize responses by type and formulate inquiries. The system accurately classifies Arabic healthcare inquiries, achieving precision, recall, and F1 scores of 89, 87, and 83%, respectively.

NLP techniques and models are strategically employed in closed-domain question analysis and comprehension to enhance the classification and interpretation of questions within specialized domains. These readily accessible systems typically perform well by accurately answering questions and demonstrating a solid mastery of field-specific topics, as seen by their high scores annotations and other performance measures.

The approaches utilize performance indicators in various QASs. These systems have both open and closed domain needs. Open domain requirements utilize rule-based methodologies and advanced machine learning techniques to enhance system efficiency significantly. Within other restricted domains, a range of variations peculiar to the operations of natural language processing exists in that domain. The provision of healthcare and education requires specialized procedures that are employed within restricted boundaries. Additional metrics used to evaluate system performance include precision, recall, and accuracy. Table 2 compares Arabic Question Answering Systems (QASs) to help understand their pros and cons.

Question analysis researchers in QASs struggle to create new systems and methods to address these concerns. Arabic is inflectional and derivational, adding complexity. Varied affixes give Arabic words varied forms (Alwaneen et al. 2022 ). Document sparsity increases with variety, complicating query analysis and passage retrieval. Developers have created word analysis techniques to address these linguistic issues. These Arabic word-processing technologies recognize primary linguistic forms and accommodate morphological variability. Table 3 lists these utilities' URLs.

Each tool listed uniquely enhances Arabic language processing in Natural Language Processing (NLP). Here's an overview of these tools in paragraph form:

Arabic text stemming is the primary application of the Khoja Stemmer (Khoja and Garside 1999 ). Stemming, which distills words to their most basic or root form, is one of the most essential NLP operations. With Arabic's intricate morphology, this is particularly crucial. In contrast, Arabic tokenization and stemming functionalities are offered by the ISRI toolkit, which is a part of the Natural Language Toolkit (NLTK). Tokenization divides text into discrete words or tokens and is a crucial step in text analysis.

Arabic-NER stands out partly due to its expertise in Named Entity Recognition (NER) (Sadallah et al. 2023 ). It does well by detecting and classifying the names, places, and organizations important in extensions within Arabic text. This is enhanced by AraMorph, which mainly targets the analysis of morphological Arabic words. Because Arabic words are composed of a complex set of structures, if used in research, the ability of AraMorph to research word construction and structure has been of great assistance.

Most other applications do the still time-consuming process by tracing the assignment of word classes (nouns, verbs, etc.), which are used to envisage the sentence structure. Those features are comprised of tokenization, part-of-speech (POS) tagging, NER, lemmatization (shortening words to the root forms according to the entry in the dictionary, in the case of verbs, pronouns, adverbs), and parsing, including the complexities of dependency parsing, such as Stanford CoreNLP (Diab et al. 2004 ; Green and Manning 2010 ; Monroe et al. 2014 ).

NooJ (Najar et al. 2022 ; Mourchid 2017 ; Kassmi et al. 2019 ; Bounoua et al. 2018 ) is a versatile linguistic software that is capable of processing multiple languages, including Arabic. The esteemed position is established on linguistic or computational prowess, enabling it to effectively manage various NLP techniques that are valuable in evaluating Arabic language data.

AraNLP (Althobaiti et al. 2014 ) is a powerful tool for tokenization, morphological analysis, NER, and stemming in one. Second, the tool identified by SAFAR (Bouzoubaa, et al. 2021 ) can provide vibrant services, including morphological analysis, stemming, and POS tagging to even the service of many layers of language processing, like syntactic analysis.

MADAMIRA (Pasha, et al. 2014 ) is a tool that integrates the derived features of MADA and AMIRA (Diab 2009 ) for Arabic processing. It was one of the powerful tools known for morphological analysis, POS tagging, stemming, and NER capabilities. It handles the complexities emanating from Arabic grammar and syntax structures very well. Camelira (Obeid et al. 2022 ) picked it up and continues providing these essential services after MADAMIRA.

CAMeL Tools (Obeid, et al. 2020 ) offers morphological analysis, sentiment analysis, POS tagging, dialect identificationNER, and NLP fundamentals. Another tool in this space that provides NER, segmentation, stemming, and POS labeling is Farasa.

Arabic WordNet (AWN) is distinct in that it functions as a semantic database tailored to the Arabic language and operates without processing tools. Moreover, AWN sets with synonyms, hyponyms, and hypernyms provide helpful backing for thorough Arabic analysis. These characteristics offer significant advancements in Arabic natural language processing, particularly in classification and question analysis. AWN significantly enhances Arabic Question Answering Systems (QASs) performance and capacities by addressing specific challenges inherent in the Arabic language.

3.4 Resources

Although they adhere to the first stage of question analysis in this domain, most of the time, it is still invisible that specialized datasets exist. What best illustrates this: Although many datasets are available for Arabic QAS, not all are made with question analysis in mind. As a result, further specialist resources are still needed in this area. Several academics have lately begun to modify the creation of specialized datasets in this approach; one notable example is the development of Li and Roth's work (‘Learning Question Classifiers’. 2024 ; Hamza et al. 2021 ). Hamza et al. ( 2021 ) Take note. Nevertheless, Arabic-language datasets that were created explicitly for this purpose are absent. The usefulness and performance of QAS are dependent on question analysis and classification, which explains why this gap is so significant. Although there is a rising number of research works and resources available in the field of Arabic natural language processing (NLP), it is noted that the current phase of insufficiently focused datasets is entirely irrelevant as new, more sophisticated Arabic QAS is being built. Therefore, the gap identifies a crucial area for further study and advancement. Undoubtedly, there is still a great demand for specific datasets in this field to support the intricacies and unique characteristics of question analysis and categorization in Arabic. The development and availability of such data sets will not only help to bring the relevant research closer together and more focused, but it will also raise the overall effectiveness and capacities of Arabic QAS to a new level.

3.5 The impact of large language models on question analysis in QASs (integrating traditional methods with modern innovations)

This literature review only considers papers published up until 2023. The year 2024 achieved incredible strides in NLP were made, mainly due to Large Language Models (LLMs), so careful consideration will have to be made to put these new developments in perspective. LLMs like GPT-4 and ArabianGPT with variants like LLaMA changed the paradigm of the erstwhile Question Analysis phase in QASs. The Question Analysis phase usually has been fundamental in guaranteeing the clarity, pertinence, and handling of the questions' complexity, especially for a morphologically rich language like Arabic. Nevertheless, LLMs can handle complex questions without explicit components for question analysis.

For example, the traditional process for generating question–answer pairs is considered very meticulous for high quality in ArabicaQA, which includes article selection, question generation, filtering, and human answer categorization. Since the process is well-defined, it guarantees that questions are correctly formed and their answers are correct (Kamalloo et al. 2023 ; Abdallah et al. 2024 ). However, recent work by Karpukhin et al. (2020) and Izacard and Grave (2021) from "Evaluating Open-Domain Question Answering in the Era of Large Language Models" (Kamalloo et al. 2023 ) suggests that top-of-the-line LLM QASs could also be very efficient when retrieval and generation are combined—possibly bypassing question parsing as traditionally conceived.

Further research has been conducted, such as the survey "Complex QA and Language Models Hybrid Architectures," which has focused on hybrid models that marry classic QA components with LLMs. This has underscored how such models might use state-of-the-art reasoning and decomposition techniques in handling complex questions. Similarly, the paper "Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity" proposes an adaptive framework that self-adjustingly retrieves relevant information and integrates it to handle varying question complexities without explicit analysis of the questions themselves (Daull et al. 2023 ). Furthermore, there is an all-encompassing survey entitled "Large Language Models: A Survey" that takes stock of the broad applications and impacts of LLMs, studying how models like GPT-4 and LLaMA raise the performance of QASs since explicit parsing of questions is less necessary (Minaee et al. 2024 ).

Overall, despite LLMs having huge potential to make the QA process smoother by reducing the need for question analysis, there is a need to consider application requirements and not compromise on the balance between efficiency and accuracy. Literature reviews about the recent development of LLMs will significantly improve the performance of QAS.

4 Analysis and discussion

The research thoroughly evaluates the current patterns and recent advancements in analyzing and categorizing Arabic questions. It symbolizes the reverberation of past events. It primarily impacts the ability to depict the fundamental patterns, necessitates a profound comprehension, sustains a comprehensive perspective, and entails a critical analysis of the advancements and obstacles in a scientific investigation of a specific subject or field. This study examines solutions to the challenges of implementing the Arabic Language Project.

4.1 Analysis

This section involves a thorough analysis and assessment of the data to extract in-depth insights and comprehend the intricacies of the topic. This comprehensive analysis aims to detect patterns, examine the methods, and assess the progress in analyzing and categorizing Arabic questions. By conducting a thorough evaluation, this procedure is anticipated to uncover the advantages of these systems and pinpoint areas that need enhancement.

All the literature reviews and studies included in this survey under detailed analysis have been allocated. Under one column, every research study was listed, with one cell containing the name of every survey. Individual columns have been given to categories corresponding to the features. On encountering any category discussed by a study, a '1' was marked, and on the nonavailability or absence of any writing about it, a '0' was marked. Performance metrics were recorded as actual values. If a study did not provide the metric, then an empty cell was left.

In such a view of this structured dataset, we developed a standardized evaluation framework to make estimates with performance metrics for the reviewed techniques of question analysis. Homogeneous and comparative study among the different methods is given by this framework, which increases the reliability and comparability of the results. Much more precisely, Python was used to compute such different studies for each feature distribution analysis to show trends, strengths, and weaknesses of approaches under study. Extraction of the correlation matrix: The correlation matrix was extracted using Python too, showing various relationships among the features and performance metrics.

This thorough analysis aims to reveal patterns, investigate methods, and assess the achievements in developing the Arabic question structure analysis and categorization. This envisaged method should demonstrate the benefits of those systems and clarify the remedial measures by analyzing the weak spots.

4.1.1 Domain types and question types

Figure  2 shows academic question distribution and classification. Every year except for 2014 and 2023, research articles are "Open." Research on question analysis and classification is broad. It may indicate a movement toward cross-field methodologies and procedures.

Annual trend analysis of category distribution from 2014 to 2023

This distribution analysis, as shown in Fig.  2 , reveals a consistent focus on the "Open" domain, suggesting a preference for generalizable research in the field. The limited but present activity in specialized domains indicates potential areas for future exploration and growth.

4.1.2 NLP methods and techniques

As shown in Fig.  3 , the frequency of NLP methods has brought forward a strong preference for fundamental steps in preprocessing; tokenization and normalization have taken the lead in frequency. These basic methods lay the foundation for other more sophisticated text analyses and underscore their relevance in NLP. Stop-word removal and part-of-speech tagging are highlighted, and it further brings out the prominence where much attention has to be given to refining text data for meaningful analysis. It also displays a variety of specialized methods used, ranging from query expansion to named entity recognition and further reaching with different word embedding techniques. This, in turn, clearly points out that there tends to be a general trend of mixing traditional approaches with modern ones. Most other methods have been leaning toward deep learning and advanced methods to understand the language. Combining conventional and state-of-the-art techniques would reflect the dynamism NLP research entails and, thereby, a balanced approach synthesizing the former with the latter.

NLP techniques used frequency

This review of some advanced NLP techniques shows appreciable results for the domain of Arabic Question Analysis, as shown in Fig. 4 . The M-TF-IDF was very successful, as the average precision comes out to be 79.13, the average recall is 78.72, and the F1 measure is 94. So really, in terms of precision, accuracy, and F1 values, strong results have been observed. It goes further to give credence to an overall fusion with Contextual Word Embeddings and taking on very high precision scores at the highest level, which would indicate having substantial positive effects on the performance. This method is chunking-based with chunking techniques applied, reaching a very high percentage of 87. Therefore, it can be mentioned that it allows for very effective text division into parts corresponding to linking syntax in the core method. On the other hand, the Stop Word Removal gives commendable precision at 86.065 and accuracy at 78.23, but it did not specify the F1 and MRR scores. With precision scores high and moderate in accuracy, it reflects the parsing potential of giving relevant output when applied accordingly. The respective few studies had a look at the Mean Reciprocal Rank (MRR) metric, in addition to some performance metrics of the technique that are missing in most studies. This omission suggests that these metrics were either not evaluated or not reported in the reviewed studies. In such a context, this paper considers the mean of effectiveness of different academic papers, underlining the fact that different techniques presented lead to effectiveness achieved at different levels in real-life applications, based on certain contexts and task requirements. This, on the other side, would lead to the need to critically evaluate NLP techniques in each scenario, but very important in Arabic Question Answering Systems, so that these could be put into actual practice. In Al-Chalabi et al. ( 2015 ) show an example of the difference in performance when the query expansion techniques are tested with or without having applied them.

Performance metrics of NLP techniques in QASs

The empirical correlation coefficients, as shown in Table  4 , reveal the nuanced effects of various NLP techniques on the performance of question analysis systems. Contextual word embeddings, while positively associated with Precision and F1 Measure, indicate a tendency to improve the retrieval of relevant information and the harmonic mean of precision and recall. Yet, they exhibit a negative relationship between accuracy and the ability to rank answers effectively (MRR). Similarly, disambiguation is positively correlated with precision, suggesting its strength in pinpointing accurate information, but its negative correlation with Accuracy and F1 Measure may imply a trade-off, possibly reducing overall question-answering correctness and the balance of recall with precision. Techniques like a fusion of word embeddings and handling OOV share this pattern, enhancing Precision and F1 Measure at the possible expense of accuracy. Keeping Important Interrogative Words shows a slight positive impact on precision, indicating some success in identifying relevant content; however, its negative correlation with F1 Measure hints that it may not consistently aid in achieving a balanced Precision-Recall performance. These correlations underscore the complex interplay between different NLP techniques and various aspects of performance metrics, suggesting that compromises in others may accompany improvements in certain areas.

4.1.3 Classification techniques

In question classification, rule-based approaches such as regular expression and context free grammer smash with the adaptability of the machine learning model, which might lead to creating an entirely new path. After rigorous testing, it has been noticed that better results for accurately decoding such complicated textual inputs could be obtained with several advanced deep learning models, like M-BiGRU-CNN and M-TF-IDF, than with classical models, such as SVM and Logistic Regression. In some situations, hybrid systems have proven successful or have even been accused of being a rule-based, tailored system. Current work points to the pace at which NLP technology is growing, and it has been suggested that more hybrid systems or holistic evaluation frameworks be suggested. We have witnessed diversity in the NLP technology proposed, some of which can be found only from this work, and varied solutions have been proposed based on the context in consideration of the data and evaluation constraints. This is a systematic review of the field using methods described in NLP to analyze multiple queries. Poor data availability on method performance can cause bias or incomplete studies. A lack of success in one of those performance measures doesn't necessarily mean no success in the rest. Hence, the more vividly complex nature of NLP. All of these are shown in the distribution analysis in Fig.  5 . The correlations show that different strategies have a distinct impact on system performance, pointing out the need to carry out multidimensional evaluations of NLP techniques.

Comparative analysis of average performance metrics for question classification across different question classification techniques

4.2 Discussions

Arabic is a language of immense and rich linguistic heritage. Its language is full of structural complexities that combine to make it one of the most difficult challenges to question analysis. This section first tries to bring some Arabic grammatical subtleties to the fore and then proceeds to discuss how these subtleties come to bear on the interpretation of questions. This is our hope: to shed some light on these special issues of reading and processing Arabic questions, an issue of paramount importance in developing effective Arabic QASs.

Challenges in Arabic NLP(Abdelmegied et al. 2019 ; Samy et al. 2019 )

The Arabic language indeed owns a kind of internal linguistic structure with several intrinsic properties in such a way that Natural Language Processing (NLP) finds it very challenging.

Rich Morphology: Arabic language carries a rich and complex morphology, needing powerful algorithms for text analysis. The complexity is such that it requires the identification of accurate roots or stems, understanding morphological patterns, and identifying parts of speech, numbers, cases, and moods.

Nonlinear Root-Pattern Morphology: Arabic has a nonlinear root-pattern system. Its right-to-left script orthography and variable shapes of the letters according to word position make it a point of great complexity in language processing for both theory and practice.

Diacritics and Ambiguity: The use of diacritics as optional in the Arabic text had always provided full scope to ambiguity, as it could change the whole meaning of words to a great extent. Coupled with this, the constant omission of diacritics makes the situation worse in analyzing and interpreting the text.

Derivational and Inflectional Nature: Arabic's use of various prefixes and suffixes in word formation adds another layer of complexity, resulting in intricate word formations that challenge conventional linguistic analysis.

No Capitalization: In fact, the lack of capitalization in the Arabic language has caused, sometimes, a bit of a problem in doing, for instance, Named Entity Recognition (NER), while in other languages, capitalization separates all proper names.

Agglutination: It is derived from the Arabic word, usually from its root, by adding affixes or combinations of two or more roots. Hence, the word becomes very long and seems to be more than one word. This process of agglutination makes segmentation and analysis somewhat tricky.

Short Vowels: Short vowels in Arabic writing are optional, and this often causes ambiguities, as the same set of consonants can stand for different words according to their respective contexts.

Free Word Order: The word order is relatively free in Arabic, which exposes one of the contributing elements to its syntactic complexity and difficulty in making a sentence.

Incoherent Writing Styles: Differences in writing style based on dialectical and individual differences can be problematic in standardizing and processing Arabic text.

Typos and Errors: Typographical errors and errors are common in all languages, but in Arabic, they are much more frequent. This creates severe misinterpretations and adds another level of difficulty for NLP tasks.

Resource Scarcity: Arabic NLP represents a scarce resource and tools, like any other language with scanty full corpora and an advanced language model, which restrict effective language processing.

Complex Structure and Semantics: Not only the linguistic structural complexity but even more so, the semantic complexity, with a superimposition of cultural allusions and idiomatic expressions, makes the Arabic language a vibrant tapestry. Arabic has distinct NLP issues due to its rich linguistic structure and characteristics.

All that makes very sophisticated models and algorithms need to be applied to make some sense of this. These would be very important and pose the complexity of the challenges in Arabic morphology and its effects on NLP. The above necessity would be reached through specialized solutions and continued research to maneuver through these difficulties in various NLP applications.

Challenges and limitations in Arabic question analysis and classification

Analysis and classification of questions: Major steps in QASs are done before the latter modules' passage retrieval and answer extraction. This is a detailed analysis of the questions that are called for, so the QAS system can find and apply some of the relevant information, increasing the chances of getting the correct answer. On the other hand, imprecision in the analysis of questions may result in the retrieval of irrelevant information that may produce wrong or unrelated answers, hence lowering the effectiveness of the QAS. This naturally enhances the module and allows for a comprehensive evaluation of the question analysis and classification module. The module can be optimized to influence the efficiency and precision of the whole QAS greatly. It is also essential that this module is seriously considered before considering others since, if polished, it will significantly affect the overall performance of QAS. We shall now discuss significant reflections from the reviewed papers, diving deeper into the intricate matters of an Arabic Question Answering System.

The section synthesizes these insights, which is meant to give a more overall view of the main challenges and prospective directions identified in our research. This critical reflection and synthesis aim to contribute to the required improvement within the question analysis subsystem in Arabic QAS. We identified the technological and methodological hindrances to the development of Arabic QAS. The discussion aims to restate these essential features from a somewhat synoptic perspective of this field's pros and cons. Last, the review will encapsulate observed limitations in the current state of Arabic QAS, hence prime importance in guiding future research and development in this domain. New trends in Arabic QAS are also considered, given the fast growth in natural language processing and machine learning technologies.

The present study enlightens important features of Arabic question analysis systems. It shows that while the Disambiguation and Contextual Word Embedding techniques are precise, their inverse relation with accuracy and other metrics indicates overfitting or reduced utility in more extensive applications. Their study showed that taxonomies, question classification standards, and classifier performance measures had hit the roof. It would give future researchers the rare opportunity to develop innovative taxonomies and performance metrics that appropriately reflect the complexity of the classification issue in various scenarios (Silva et al. 2019 ).

Such a focus on the "Open" domain of Arabic QAS seems to create a gap in the domain-specific research area. This gap will support further studies related to question analysis in each discipline.

Its complex syntax and different dialects obstruct preprocessing. But language integrity needs good preprocessing. This is done in Arabic QAS with the help of Python and Java Stanford APIs. Moreover, it has a smaller number of annotated corpora and does not allow the generation of Arabic word vectors for advanced processing. Limitations on developing Arabic QAS, even with promising solutions like AraVec, are limited comprehensive linguistic resources and sophisticated text processing tools (Biltawi et al. 2021 ; Albarghothi et al. 2017 ).

All these have restricted development in this field, research, and technology; benchmarked Arabic datasets and defined corpora with which to test systems. They also translate from other languages but do not fully cover them and contain non-equivalent acronyms that make a difference in their performance. This means that the dataset could be topic-inclined, labelled, and classified precisely and consistently for one reason or another. It identifies the need for comprehensive Arabic Question Analysis and Classification data collection.

Current Arabic QAS models also lack full internet availability, which denies comparison of the systems to others in the field and hence cooperation—more, because research uses different databases. The question classification uses interrogative methods. Arabic linguists have identified 13 noun-and-particle interrogative strategies (Alwaneen et al. 2022 ; Lahbari et al. 2017a ). The unavailability of standard techniques in gathering and classifying the structures of an interrogation makes it very complex to realize the difficulty in analyzing their formal structure, let alone evaluating them. The absence of these standards makes it very difficult to have Arabic QAS designed and improved with reduced ambiguity and the complexity of question analysis.

However, this inconsistency in the measurement of the several systems presents a certain lack of unified methodology for assessment and comparing it with some platforms becomes difficult. Each system would instead choose the performance measurements it would like to publish, and which highlight its advantages rather than having a coherent picture of performance. This does not allow a level ground for comparison and clouds out the growth and innovation in the industry. Therefore, this generation and comparison of Arabic QAS will need similar performance criteria.

Significant barriers and limits have been identified; they will be critically analyzed in this study. The aim is to disarm the hurdles identified from our research findings by presenting a clear picture of the issues in this area. These constraints reflect new challenges identified through our analysis and continued concerns from past polls.

In this, it has been emphasized that a particular profound nature enshrined within Arabic Question Analysis and Classification is still to be solved. Some of the limitations identified in this research included:

Limited Innovation in Research Question Taxonomies and Performance Indicators.

Lack of Processing Tools.

The gap in Domain-Specific Research in Question Analysis.

The Necessity of Benchmark Datasets for Arabic Question Analysis.

Issues About Datasets Derived from Translation.

The Imperative of Standardization in Dataset Development.

Complexities in Identifying Interrogative Words and Handling Their Structures.

Challenges are witnessed in the comparative analysis of the Arabic QASs since there are no standardized performance metrics and accessible systems online.

Arabic question analysis systems are faced with numerous significant challenges due to the complex morphology and linguistic characteristics of the Arabic language. Analysis depth and accuracy that would be required for proper question analysis are not guaranteed by state-of-the-art taxonomies and criteria for question classification and performance metrics. Added to this is a lack of advanced processing tools to deal with the complexities of Arabic language analysis. Moreover, the study takes little or no consideration of domain-specific question analysis questions; it suggests that more elaborate techniques are required. Such a lack of benchmark datasets for Arabic question analysis hinders system development. Translation from other languages misses subtle features of Arabic and often leads to confusion and inaccuracies. Furthermore, data quality issues are not clearly identified regarding how the dataset was generated, fundamentally affecting the reliability of these systems. The intricacies of Arabic interrogative words and structures raise more complications in ensuring accurate classification and analysis.

LLMs have highly influenced even Arabic question analysis systems by facilitating the handling of questions. This can be integrating the retrieval and generation of questions with less reliance on detailed parsing usually required in high-quality question analysis systems like ArabicaQA. In one study on hybrid models that combine classical question mechanisms with LLM capabilities, instances show that complicated questions can quickly be dealt with through advanced inference without explicit analysis. While such innovations have made things a lot easier, efficiency still has to be balanced against accuracy in the interests of question analysis applications. This is most especially true with morphologically rich languages like Arabic.

These challenges require the creation and standardization of datasets exactly suited for Arabic question analysis. New parsing algorithms and integration of LLMs need to be developed to efficiently manage the complexity of Arabic syntax. Instigate standardized test metrics that allow overlay comparison between different systems. Some critical ways to enhancement of Arabic question analysis systems are encouraging collaborations to create datasets, developing open-source tools, and setting universal standards for dataset development and system evaluation. Involving such solutions in practical implementations would result in projects creating comprehensive annotated Arabic question data sets and robust context-aware NLP tools, including LLMs, designed to handle the unique linguistic features of Arabic. If these issues are addressed, they will secure for the Arabic question analysis systems maximum achievements regarding accuracy and effectiveness.

5 Conclusions and future directions

This survey provides a complete overview of the research analysis of questions, especially on Arabic Question Answering Systems. On the same line, one of the studies found that deep learning models support traditional machine learning algorithms through a careful comparison of the usage of the methods in natural language processing. M-TF-IDF greatly helps in language processing. Parallel to this, SVM and Logistic Regression are useful in classification problems. The rule-based and hybrid systems shown in this research effectively solve this task and may indicate the need for such language processing solutions. An initiative like this has further displayed various challenges in the field, such as variances on the syntactical and dialectical level, the absence of Arabic natural language tools, challenges in the standardization of data sets, and benchmark construction.

Proceeding further, this survey delineates the trajectories of future research in the Arabic QAS domain, with a focus on several key areas:

Development of Comprehensive Datasets: One of the critical tasks includes the development of comprehensive and diverse datasets. The data will play a key role in training highly developed deep learning models and consequently facilitate their dealing with the vast array of questions pertinent to Arabic QASs.

Testing Framework: A standard testing framework must be established and complied with in benchmarking and comparison with other systems for accuracy. This standardization will make the process reliable and may lead to the validation and performance measurement claims of those systems.

Question Classification Hybrid Approach: The approach, therefore, must be a hybrid one, as it tries to strike a middle ground by marrying linguistic patterns with machine learning techniques. Hence, this hybrid makes the precision of rule-based logic available to ML adaptability, standing efficiently in vast variations of language-in-question classification tasks, especially with Arabic QASs.

Elevated Question Analysis Factors: Add in the question analysis modules the process of detection of the interrogative words and type, analysis of tense, aspect, and consideration made on the specific context/field of the question. Such enhancements will significantly improve question analysis's efficiency, relevance, and accuracy.

Multi-Question Handling: This will allow the detection and processing of more than one question from the user within a single query. It would enable the system to promptly handle even those complex, natural language queries, bringing it in sync with the developing landscape of user interactions.

Improving Arabic question analysis can benefit from techniques used for other languages, particularly low-resource ones, by leveraging transfer learning, data augmentation, cross-lingual embeddings, and multilingual training, as demonstrated in recent studies like Conneau et al. ( 2020 ) and Artetxe et al. ( 2018 ).

Integrating LLMs with semantic understanding technologies for in-depth comprehension of deeper meanings and nuances of complex Arabic queries. This will be achieved by having semantic Web technologies, ontology-based classification, and context-aware processing systems go hand in glove with the LLMs. Thirdly, it would therefore not be impossible for LLMs to make muscle use of these technologies to bring out better the intent and semantic relationships within questions in specialized domains or idiomatic expressions common in various Arabic dialects. Such integration will contribute to increasing the accuracy of responses and enhancing the system's ability to manage abstract and context-dependent questions, hence setting the limit for the depth of what automated question analysis systems have understood and responded to in real-world scenarios.

Proceeding further, these significant points give a definite shape to the agendas of future research in the domain of Arabic QAS. Firstly, one of them is the need for comprehensive and diverse datasets with an essential role in the training of state-of-the-art deep learning models so that they turn out helpful in serving a wide variety of questions related to Arabic QAS. Secondly, the setting up of a standard testing framework will help have reliable benchmarking and thus comparison with any other system. Thirdly, a hybrid model in question classification will integrate linguistic patterns with pattern-based machine learning methods for better precision and adaptability. Moreover, methods for detecting interrogative words, tense, aspect, and context can further optimize question analysis for better accuracy. Furthermore, multi-question handling will make the system efficient in attending to complex questions. Additionally, Arabic question analysis can leverage some techniques from low-resource languages, such as transfer learning and cross-lingual embeddings. Lastly, integrating large language models with semantic understanding technologies will enable deeper comprehension of complex Arabic queries, thus improving response accuracy. The primary tasks that would probably dominate further research in Arabic QASs are comprehensive datasets, standardized testing, hybrid classification approaches, advanced question analysis, multi-questions, cross-lingual techniques, and integration with LLMs.

This research, therefore, goes further to cast in and put beyond dispute the correct and encouraging path these methods are now driving the Arabic question analysis procedures. This approach ensures that any necessary adjustments are made and identifies significant flaws.

Consequently, this enables the formulation of practical and beneficial recommendations for enhancing the domain. It intends to go beyond being a resource for usage within academic circles; instead, it aims to kick-start activities in developing tools and datasets specifically suitable for question analysis in Arabic Question Answering systems. Upon further study, it becomes evident that there are opportunities for additional innovations and advancements in Arabic Question study. This also significantly increases awareness of this field's state of the art.

Data Availability

No datasets were generated or analysed during the current study.

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Formulation, M.E.; methodology, M.E and M.D; software, M.D.; validation and investigation, M.E. and M.D.; formal analysis, M.E. and R.E; resources, M.E.; preparing original draft, M.E.; review and editing of M.D.; supervision, R.E.; funding and visualization, M.D. All authors have read and agreed to the published version of the manuscript.

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Essam, M., Deif, M.A. & Elgohary, R. Deciphering Arabic question: a dedicated survey on Arabic question analysis methods, challenges, limitations and future pathways. Artif Intell Rev 57 , 251 (2024). https://doi.org/10.1007/s10462-024-10880-6

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Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate cell shape and colony dynamics in human induced pluripotent stem cell (hiPS cell) colonies. This model aimed to first mimic and then explore the dynamics observed in real-world hiPS cell cultures. Initial outputs showed great potential, seeming to mimic small colony behaviors relatively well. However, longer simulations and quantitative comparisons revealed limitations, particularly with the CPM component, which lacked long-range interactions that might be necessary for accurate simulations. This challenge led us to thoroughly examine the hybrid model's potential and limitations, providing insights and recommendations for systems where cell-wide mechanics play significant roles. The CPM supports 2D and 3D cell shapes using a Monte Carlo algorithm to prevent cell fragmentation. Basic "out of the box" CPM Hamiltonian terms of volume and adhesion were insufficient to match live cell imaging of hiPS cell cultures. Adding substrate adhesion resulted in flatter colonies, highlighting the need to consider environmental context in modeling. High-throughput parameter sweeps identified regimes that produced consistent simulated shapes and demonstrated the impact of specific model decisions on emergent dynamics. Full-scale simulations showed that while certain agent rules could form a hiPS cell monolayer in 3D, they could not maintain it over time. Our study underscores that "out of the box" 3D CPMs, which do not natively incorporate long-range cell mechanics like elasticity, may be insufficient for accurately simulating hiPS cell and colony dynamics. To address this limitation, future work could add mechanical constraints to the CPM Hamiltonian or integrate global agent rules. Alternatively, replacing the CPM with a methodology that directly represents cell mechanics might be necessary. Documenting and sharing our model development process fosters open team science and supports the broader research community in developing computational models of complex biological systems.

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• Published: 05 August 2024

The next frontier in immunotherapy: potential and challenges of CAR-macrophages

• Jing Li 1 ,
• Ping Chen 2 &
• Wenxue Ma 3  

Experimental Hematology & Oncology volume  13 , Article number:  76 ( 2024 ) Cite this article

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Chimeric antigen receptor macrophage (CAR-MΦ) represents a significant advancement in immunotherapy, especially for treating solid tumors where traditional CAR-T therapies face limitations. CAR-MΦ offers a promising approach to target and eradicate tumor cells by utilizing macrophages’ phagocytic and antigen-presenting abilities. However, challenges such as the complex tumor microenvironment (TME), variability in antigen expression, and immune suppression limit their efficacy. This review addresses these issues, exploring mechanisms of CAR-MΦ action, optimal construct designs, and interactions within the TME. It also delves into the ex vivo manufacturing challenges of CAR-MΦ, discussing autologous and allogeneic sources and the importance of stringent quality control. The potential synergies of integrating CAR-MΦ with existing cancer therapies like checkpoint inhibitors and conventional chemotherapeutics are examined to highlight possible enhanced treatment outcomes. Furthermore, regulatory pathways for CAR-MΦ therapies are scrutinized alongside established protocols for CAR-T cells, identifying unique considerations essential for clinical trials and market approval. Proposed safety monitoring frameworks aim to manage potential adverse events, such as cytokine release syndrome, crucial for patient safety. Consolidating current research and clinical insights, this review seeks to refine CAR-MΦ therapeutic applications, overcome barriers, and suggest future research directions to transition CAR-MΦ therapies from experimental platforms to standard cancer care options.

CAR-MΦ offers an innovative approach to treating solid tumors, addressing the limitations of traditional CAR-T therapies.

CAR-MΦ eliminates tumor cells and boosts other immune cells’ effectiveness.

Strategies are being t developed to improve CAR-MΦ targeting and cancer cell eradication.

CAR-MΦ is combined with other treatments to enhance overall efficacy.

Challenges and safety concerns, including side effects of CAR-MΦ therapies, are Beijing addressed.

Immunotherapy has revolutionized cancer treatment by leveraging the body’s immune system to detect and eradicate malignant cells [ 1 ]. The field has seen substantial advancements over the past decade with the emergence of immune checkpoint inhibitors, cancer vaccines, and adoptive cell transfer therapies, each contributing to a significant shift in oncological therapeutic strategies [ 2 , 3 , 4 ]. Among these innovations, Chimeric Antigen Receptor T-cells (CAR-T) and Natural Killer cells (CAR-NK) represent breakthrough therapies [ 5 , 6 ]. CAR-T cell therapy has shown exceptional efficacy in treating hematologic malignancies by reprogramming T cells to target and destroy tumor cells specifically [ 5 ]. Although CAR-NK therapies are still in the experimental stages, they have shown promise in offering similar therapeutic benefits but with potentially fewer adverse effects, such as cytokine release syndrome (CRS) and graft-versus-host disease (GVHD), which are more common in CAR-T cell treatments [ 7 , 8 , 9 , 10 ].

However, applying these cellular therapies to solid tumors has been fraught with challenges [ 5 , 11 ]. The primary obstacles include the immunosuppressive nature of the tumor microenvironment (TME), the heterogeneity of tumor antigens, and physical barriers that restrict cellular infiltration into tumors [ 12 , 13 , 14 ]. These challenges have sparked significant debate and exploration within the research community, as there is a consensus that overcoming these barriers could unlock new therapeutic potentials for solid tumors [ 15 , 16 ].

CAR macrophages (CAR-MΦ) may offer strategic benefits in reshaping the TME and triggering a comprehensive immune response due to their phagocytic nature and antigen-presentation capabilities, which could lead to more sustained tumor control [ 17 ]. This contentious backdrop has led to exploring CAR-MΦ as a novel therapeutic avenue. Macrophages, known for their roles in tissue homeostasis, inflammation, and immune surveillance, are engineered to express chimeric antigen receptors [ 18 , 19 ]. This approach aims to harness their inherent phagocytic nature and ability to modulate the TME, positioning them as potentially effective agents in combating solid tumors [ 20 , 21 , 22 ]. Despite the theoretical benefits, considerable controversy exists regarding the efficacy, safety, and practical application of CAR-MΦ [ 22 , 23 ]. Current knowledge is limited, particularly in direct clinical outcomes and mechanistic understanding of CAR-MΦ actions within varied TMEs [ 13 , 24 , 25 ].

Structural details and potential synergy with checkpoint inhibitors

Structural details of car-mφ.

The structure of CAR-MΦ is crucial for their function and therapeutic efficacy. CAR-MΦ are typically engineered to express CARs that include an extracellular antigen-binding domain derived from an antibody’s single-chain variable fragment (scFv). This domain is linked to intracellular signaling domains, which are crucial for activating macrophages upon antigen engagement [ 26 ]. These signaling domains often include co-stimulatory molecules such as CD28 or 4-1BB, which enhance macrophage survival, proliferation, and phagocytic efficacy [ 8 , 19 ].

Potential synergy with checkpoint inhibitors

CAR-MΦ therapy’s potential synergy with checkpoint inhibitors is a promising avenue for enhancing anti-tumor efficacy. Checkpoint inhibitors, such as those targeting PD-1/PD-L1 and CTLA-4 pathways, block inhibitory signals that dampen immune responses, thereby reactivating T cells to attack tumors [ 27 , 28 ]. Combining CAR-MΦ with checkpoint inhibitors aims to overcome the immunosuppressive TME, thus enhancing the overall therapeutic outcome [ 29 ]. Recent studies have demonstrated the synergy between CAR-MΦ and checkpoint inhibitors. Yang et al. found that CAR-MΦ engineered with anti-PD-L1 scFv showed enhanced anti-tumor efficacy in preclinical models [ 30 ]. Harrasser et al. reported that localized delivery of an anti-PD-1 scFv boosts the antitumor activity of ROR1 CAR-T cells in triple-negative breast cancer (TNBC) [ 31 ]. Li et al. showed that combining CAR-MΦ with anti-CTLA-4 therapy enhances tumor cell phagocytosis and promotes a robust immune response [ 32 ].

Clinical efficacy and safety

Clinical evidence.

The clinical exploration of CAR-MΦ is rapidly progressing, particularly for solid tumors where traditional CAR-T therapies face significant challenges [ 16 , 18 , 33 , 34 ]. Current clinical trials primarily focus on assessing CAR-MΦ’s efficacy in reducing tumor mass and evaluating their safety for patients who have exhausted conventional treatments. Initial findings show CAR-MΦ can effectively localize to and persist within tumor sites, providing promising insights for ongoing and future research [ 18 , 35 ]. However, comprehensive outcome data and extended follow-up are needed to understand CAR-MΦ’s long-term efficacy and safety [ 36 ].

One ongoing clinical trial, NCT04660929, is a Phase I study evaluating CAR-MΦ for treating HER2-overexpressing solid tumors. This trial includes patients with various HER2-positive cancers, such as breast, bladder, and lung cancers, and focuses on assessing the safety and preliminary efficacy of CAR-MΦ. Initial findings have shown that CAR-MΦ therapy is safe and well-tolerated, with some indications of anti-tumor activity, including tumor regression and enhanced T-cell infiltration at the tumor site [ 37 ]. However, extended follow-up is necessary to determine this therapeutic approach’s long-term benefits and potential risks.

Preclinical studies have demonstrated CAR-MΦ’s unique capabilities, particularly their ability to modulate the complex TME, supporting immune-mediated tumor destruction [ 17 , 19 ]. These studies have shown that CAR-MΦ not only directly attacks tumor cells but also transforms the typically suppressive TME into a more active, anti-tumor environment [ 34 , 38 ]. By secreting pro-inflammatory cytokines and chemokines, CAR-MΦ recruits and activates other immune cells, suggesting a significant role in enhancing the efficacy of combination immunotherapies [ 30 , 39 ].

Safety profile

The development and advancement of CAR-MΦ therapies bring promising therapeutic opportunities and significant safety considerations that mirror those observed with CAR-T cell therapies [ 40 ]. Both are known for their potential to cause severe adverse effects such as CRS and neurotoxicity due to their robust cytokine production capabilities [ 41 , 42 ]. However, macrophages’ intrinsic regulatory functions in managing inflammation suggest CAR-MΦ might control cytokine release more effectively, underscoring the need for research into their unique cytokine dynamics [ 43 , 44 ].

CRS is a critical concern previously well-documented in CAR-T therapy, manifesting as a systemic inflammatory response that leads to life-threatening [ 5 , 45 , 46 ]. Similar risks are possible with CAR-MΦ therapies [ 19 ]. However, the distinct role of macrophages in cytokine regulation may result in different CRS dynamics, necessitating tailored strategies for anticipation, monitoring, and management [ 34 , 42 ]. Recent studies suggest that engineering CAR-MΦ to express IL-10 can mitigate CRS while maintaining anti-tumor efficacy [ 19 , 47 ].

Another safety concern is hemophagocytic lymphohistiocytosis (HLH) and macrophage activation syndrome (MAS), involving excessive immune activation and organ damage [ 48 , 49 ]. This is particularly relevant to CAR-MΦ therapies due to their role in these conditions [ 50 , 51 ]. Ongoing vigilance in monitoring engineered cell activation and inflammatory responses is crucial to prevent HLH/MAS.

Significant gaps remain in understanding how risks from CAR-T therapies translate to CAR-MΦ therapies [ 38 , 52 ]. Questions include how CAR-MΦ modulates cytokine output and whether this modulation can be controlled to prevent adverse effects like CRS [ 33 , 39 , 53 ]. Furthermore, the long-term implications of CAR-MΦ therapy, especially concerning potential chronic inflammation or immune dysregulation, and the specificity of CAR-MΦ targeting to minimize off-target effects, need further exploration [ 54 ].

Comprehensive preclinical and clinical research on the unique safety dynamics of CAR-MΦ therapies is essential [ 17 , 19 , 34 ]. Developing accurate monitoring protocols and effective management strategies for potential adverse effects is imperative. Moreover, a deeper understanding of CAR-MΦ interactions with the immune system is crucial for maximizing therapeutic potential, mitigating risks, and integrating CAR-MΦ therapies into clinical oncology practice [ 51 , 55 ].

Figure 1 below provides a detailed representation of the essential progression, intricate dynamics within the TME, and critical safety considerations associated with CAR cell therapies.

Overview of CAR Cell Therapies: Clinical Trials, TME Interaction, and Safety Profiles. ( A ) Clinical Trials Overview: This panel illustrates the stages of clinical trial progression for CAR cell therapies, from pre-clinical lab research to Phase IV, detailing the evaluation of treatment effects in humans, safety and efficacy assessments, and the long-term impact of treatments. ( B ) Mechanisms of Action in the TME: Diagram displaying the diverse cellular composition and cytokine environment of the TME. The relationships and influences between different cell types and secreted cytokines are highlighted, showing the dynamic interactions within the TME that impact therapy outcomes. ( C ) Mechanism Differences between CAR-MΦ, CAR-T, and CAR-NK in Killing Solid Tumors: This segment compares the functional approaches of CAR-MΦ, CAR T-cells, and CAR natural killer cells (CAR-NK in combating solid tumors, emphasizing the unique mechanisms like phagocytosis by CAR-MΦ, antigen-specific T cell activation by CAR-T, and direct cytotoxicity along with antibody-dependent cellular cytotoxicity (ADCC) by CAR-NK. ( D ) Safety Profile: Outlines the critical safety concerns associated with CAR cell therapies, including CRS, neurotoxicity (ICANS), and macrophage activation syndrome (MAS). The panel describes the progression of CRS symptoms from mild to severe, details the cellular and molecular processes involved in ICANS, and explains the various macrophage polarization states in MAS, along with their associated cytokines

Recommendations for further research

As the potential of CAR-MΦ therapies unfolds, a comprehensive understanding of their clinical implications, particularly regarding safety and efficacy, is essential [ 39 , 56 ]. Insights from current clinical trials are invaluable, yet they also highlight substantial gaps in understanding, especially concerning long-term impacts and broader applicability across various cancer types [ 18 , 37 ].

Expanding the scope of clinical trials is crucial for thoroughly assessing the therapeutic potential and safety profile of CAR-MΦ across a broader spectrum of cancer types [ 57 ]. This expansion involves increasing the number of trials and including a diverse range of participants to explore how different demographics respond to CAR-MΦ therapy. Additionally, investigating CAR-MΦ interactions with other cancer treatments, such as chemotherapy or immunotherapy, could provide insights into potential synergistic effects or complications [ 17 , 58 ]. Experimenting with various CAR designs and administration strategies could also optimize the balance between efficacy and safety, improving the overall outcomes of CAR-MΦ therapies [ 56 , 59 ].

There is also a pressing need for long-term follow-up studies to understand the sustained impact of CAR-MΦ treatments on patients. These studies are critical for evaluating the durability of therapeutic benefits, potential late-onset adverse effects, and overall quality of life post-treatment [ 60 ]. Understanding the long-term effects of CAR-MΦ therapy on the immune system, including possible impacts on immune memory and susceptibility to infections or other diseases, is vital [ 34 , 61 ].

Despite promising advances in CAR-MΦ research, several significant controversies and unanswered questions remain. Debates continue over the best strategies for engineering and administering CAR-MΦ, focusing on maximizing efficacy while minimizing risks. The challenges of defining the optimal configuration of CAR constructs and the best delivery methods are compounded by significant regulatory and ethical questions, particularly regarding patient consent processes and trial inclusion criteria [ 62 , 63 ].

To fully harness the therapeutic potential of CAR-MΦ and ensure their safe integration into clinical oncology, it is essential to expand clinical trials and conduct detailed long-term follow-up studies [ 64 ]. These efforts are crucial for filling current knowledge gaps and addressing broader controversies and challenges in the field. As research continues, these focused efforts will help pave the way for CAR-MΦ therapies to transition from experimental treatments to established options within the oncological arsenal, ensuring they are both practical and safe for clinical use [ 65 ].

Comparison with other CAR cells

Car-t cells.

CAR-T therapy has revolutionized the treatment of hematological malignancies such as acute lymphoblastic leukemia (ALL) and diffuse large B-cell lymphoma (DLBCL) [ 64 , 66 ]. This therapy targets and eliminates cancer cells with specific antigens, demonstrating significant efficacy. However, extending CAR-T therapy’s success to solid tumors has proven complex, revealing intrinsic limitations that underscore the challenges of applying this therapy across diverse oncological applications [ 67 , 68 ].

TME in solid tumors presents formidable physical and immunological barriers to CAR-T therapy [ 67 , 69 ]. While CAR-T cells are highly effective in blood cancers, their application in solid tumors has not met with the same success due to the TME’s complexity, which includes immunosuppressive cells, inhibitory cytokines like TGF-β and IL-10, and physical barriers that restrict CAR-T cell penetration and persistence [ 6 , 13 , 14 ]. Strategies to enhance CAR-T cell infiltration and survival within these hostile environments remain a significant focus of ongoing research [ 67 , 70 ].

CAR-NK cells

CAR Natural Killer (CAR-NK) cells are rapidly emerging as a promising frontier in adoptive cell therapies, leveraging the innate capabilities of NK cells to recognize and eliminate malignant cells without prior sensitization [ 71 ]. By engineering these cells to express specific antigen receptors, researchers have expanded their targeting capabilities and enhanced their natural cytotoxic abilities, which include direct induction of cell death and release of cytolytic granules containing perforin and granzymes [ 72 ]. Additionally, CAR-NK cells can mediate ADCC, enhancing their utility against tumors that express specific antigens [ 73 ].

The clinical applications of CAR-NK cells have shown promising results, particularly in treating hematologic malignancies such as leukemia and lymphoma [ 74 ]. However, translating these successes to solid tumors presents substantial challenges. The immunosuppressive TME in solid tumors can significantly inhibit CAR-NK cell function and persistence. In contract, the heterogeneity of tumor antigens and the potential for antigen escape pose additional hurdles to their clinical effectiveness [ 10 , 75 , 76 ].

When compared with CAR-NK cells with CAR-MΦ, both modalities encounter similar challenges in solid tumors, particularly concerning immunosuppressive TME [ 10 , 77 ]. However, CAR-NK cells may possess inherent advantages due to their cytotoxic mechanisms and ability to engage in ADCC, potentially providing a more robust and immediate response to tumor cells [ 78 , 79 ].

CAR-MΦ is making significant strides in adoptive cell therapy by utilizing the innate biological functions of macrophages to combat cancer [ 19 , 30 ]. These engineered immune cells exploit macrophages’ natural phagocytic and antigen-presenting abilities, offering a novel dimension in cancer treatment, particularly effective against solid tumors [ 19 , 34 ]. The dual functionality of CAR-MΦ allows them to reduce tumor mass by engulfing and digesting tumor cells and to process and present antigens, thereby catalyzing a broader systemic immune response against the tumor [ 34 ].

Beyond their immediate impact on cancer cells, CAR-MΦ is adept at navigating and modulating the complex and often hostile TME [ 33 , 80 ]. Their inherent migratory and infiltrative capabilities enable them to overcome physical barriers within the TME that typically shield tumor cells from immune attacks [ 13 , 81 ]. Once inside the TME, CAR-MΦ can disrupt the local immunosuppressive conditions by secreting pro-inflammatory cytokines and chemokines, making the environment more amenable to immune-mediated attack [ 36 , 82 ].

Despite these significant advantages, CAR-MΦ faces several critical challenges that limit their broader application. The field widely recognizes the difficulty in identifying specific targets on tumor cells that can be consistently recognized by the engineered receptors on CAR-MΦ, given the heterogeneity of tumor cells and the potential for antigen escape mechanisms [ 17 , 19 ]. This challenge underscores the ongoing debate over the specificity and efficacy of CAR-MΦ targeting and the need for continued research into universal tumor markers that CAR-MΦ can reliably target [ 83 ].

Moreover, like other CAR therapies, CAR-MΦ is at risk of inducing CRS, a severe side effect arising from cytokine’s rapid release into the bloodstream [ 19 ]. This safety concern mirrors those associated with CAR-T therapies and fuels further debate on the clinical viability of CAR-MΦ [ 19 ]. Addressing this risk necessitates careful CAR construct design and strategies to control CAR-MΦ activity once administered to patients [ 17 , 39 ].

To encapsulate the distinct characteristics and challenges faced by CAR-T, CAR-NK, and CAR-MΦ therapies in solid tumors, Table  2 offers a comparative overview, highlighting their respective advantages and limitations.

In conclusion, while CAR-MΦ offers unique advantages in cancer therapy through their phagocytic and antigen-presenting abilities and their capacity to modulate the TME, significant hurdles remain [ 33 , 35 ]. The challenges of targeting specificity and managing CRS, along with unanswered questions about improving the specificity of CAR-MΦ for tumor cells, enhancing their persistence in the TME, and developing effective combination therapies, continue to shape future research directions [ 35 , 67 ]. Overcoming these obstacles through innovative research and development will be crucial for fully realizing the therapeutic potential of CAR-MΦ and broadening their clinical application across diverse cancer types [ 32 ]. Addressing these issues through continued research and clinical trials is essential for advancing CAR-MΦ therapy from a promising experimental approach to a robust, clinically viable treatment option for cancer [ 30 ].

Tumor microenvironment interaction

Immunosuppressive tme.

The TME significantly impacts the efficacy of adoptive cell therapies such as CAR-T and CAR-NK therapies. The TME’s complex array of cellular and molecular components creates a hostile environment that challenges the therapeutic success of these innovative cancer treatments [ 38 , 69 ]. Figure  2 illustrates the interactions of various immune cells within the TME and their mechanisms for targeting cancer cells.

Tumor Microenvironment Interaction. This figure illustrates the interactions of various immune cells within the TME and their mechanisms for targeting cancer cells. The top row includes legends for different cell types. The central section depicts a dense network of cancer cells interspersed with various immune cells within the TME, highlighting stimulatory cytokines, including TNF, IL-1, IL-6, IL-12, and IL-18 that enhance immune responses, and inhibitory cytokines such as TGF-β, IL-4, and IL-10 that suppress immune responses. CAR-T cells attack cancer cells by releasing granzymes and perforin, leading to cell death. CAR-NK cells kill cancer cells through direct cytotoxicity using perforin. CAR-MΦ cells, with their dual role, kill cancer cells by secreting pro-inflammatory cytokines and presenting antigens. The death of cancer cells post-interaction with these CAR cells emphasizes their respective mechanisms of action. This figure underscores the complexity of the TME, and the strategies employed by CAR-T, CAR-NK, and CAR-MΦ cells to overcome immunosuppressive barriers and effectively target cancer cells

The immunosuppressive nature of the TME notably hinders the effectiveness of both CAR-T and CAR-NK cells [ 8 , 84 ]. While CAR-T cells have achieved remarkable success in hematologic cancers, their transition to treating solid tumors is fraught with difficulties due to substantial physical and biochemical barriers. These barriers include dense extracellular matrices that impede cell infiltration and various immunosuppressive cells and cytokines that restrict access to tumor cells and promote T-cell exhaustion, reducing their cytotoxic functions [ 5 , 69 ].

Similarly, despite their innate ability to recognize and kill tumor cells without prior sensitization, CAR-NK cells encounter limitations within the TME that affect their persistence and cytotoxic activity. The suppressive factors within this environment can deactivate their natural cytotoxic mechanisms and reduce their overall effectiveness against tumors [ 84 , 85 ].

Efforts to mitigate the effects of the TME on CAR therapies involve consensus-driven and innovative strategies [ 86 , 87 ]. One common approach is engineering CAR cells to express cytokines that counteract the TME’s suppressive nature [ 45 , 88 ]. For instance, incorporating genes that encode stimulatory cytokines such as IL-12 or IL-15 aims to maintain their antitumor activity within this challenging environment [ 89 , 90 ].

The use of checkpoint inhibitors alongside CAR therapies is also gaining traction. These inhibitors can block the pathways tumors use to suppress immune responses, potentially rejuvenating exhausted CAR-T cells and boosting their functionality within the TME [ 91 , 92 ].

Overcoming the physical barriers within the TME is crucial for the success of these therapies [ 93 , 94 ]. Innovations such as enzymatic degradation of the extracellular matrix and employing nanoparticles for more effective delivery of CAR cells are being explored to enhance their infiltration and persistence in tumor sites [ 95 , 96 ].

Despite significant advances, substantial gaps remain in understanding how to adapt CAR therapies effectively for solid tumors [ 67 , 68 , 70 ]. Questions persist about the optimal design of CAR constructs to improve their affinity for antigens and resistance to immunosuppressive cytokines [ 15 , 97 ]. Furthermore, understanding the long-term effects of using stimulatory cytokines within CAR constructs on the systemic immune response and patient safety is crucial [ 12 , 98 ].

As research continues to evolve, filling these gaps will be vital for enhancing the clinical applicability and success of CAR therapies in treating solid tumors. This ongoing exploration is critical to improving outcomes for patients facing these challenging conditions [ 67 , 99 ].

CAR-MΦ in the TME

CAR-MΦ offers a transformative strategy in the evolution of adoptive cell therapies, targeting the intricate dynamics of the TME [ 100 ]. These engineered macrophages aim to reprogram tumor-associated macrophages (TAMs), which tumors typically manipulate to support cancer growth and suppress immune responses [ 13 , 101 ].

By integrating CAR constructs into macrophages, researchers aspire to transform these generally suppressive immune cells into potent anti-tumor agents [ 32 ]. CAR-MΦ is engineered to recognize and destroy tumor cells, potentially reversing the immunosuppressive functions of TAMs and converting them into cells that actively bolster immune responses against the tumor [ 19 , 102 ]. This approach, however, is subject to significant debate. While some studies have shown promising results with successful reprogramming leading to tumor regression, others point out the variability of TAM behavior across different tumor types and stages, which can critically affect the outcomes of CAR-MΦ therapies [ 25 , 103 ].

In addition to reprogramming, CAR-MΦ exhibits a unique potential for beneficial interactions with other immune cells within the TME, such as T cells and NK cells [ 32 ]. These interactions, which involve antigen presentation and co-stimulation, could significantly enhance T-cell activation and immune response against tumors. Moreover, the ability of CAR-MΦ to assist NK cells might amplify natural cytotoxic responses against the tumor [ 104 ]. Despite these theoretical advantages, the effectiveness and consistency of these interactions in vivo remain a topic of ongoing research, with studies reporting variable outcomes depending on the specific conditions of the TME.

Another promising aspect of CAR-MΦ therapy is its potential synergy with checkpoint inhibitors [ 105 ]. These inhibitors, designed to block the proteins that tumors use to shut down immune responses, could be particularly effective when combined with CAR-MΦ, potentially sustaining their activation and tumor-killing ability within the typically immunosuppressive TME [ 106 ]. While there is general agreement on the potential benefits of this combination, the empirical evidence is still accumulating, and the optimal strategies for their use continue to be debated.

Despite significant advances, several critical gaps remain in understanding CAR-MΦ’s role within the TME [ 19 , 107 ]. Questions about the efficacy of TAM reprogramming in various types of solid tumors, the long-term effects of CAR-MΦ therapy on the immune system and tumor dynamics, and the optimal strategies for combining CAR-MΦ therapy with other treatments are crucial for designing a more effective therapeutic strategy [ 108 ]. Additionally, understanding how CAR-MΦ navigates the complex regulatory pathways within the immune system and identifying targets to enhance their persistence and efficacy are vital areas needing further exploration [ 8 ].

Addressing these gaps through comprehensive research and controlled clinical trials will be essential for advancing CAR-MΦ therapy from a promising experimental approach to a robust, clinically viable treatment option across various cancers. As the field evolves, these efforts will be crucial in optimizing the design and clinical application of CAR-MΦ in oncology [ 17 , 34 ].

Mechanisms of action

Antigen recognition and activation pathways.

CAR-MΦ represents a pivotal shift in cancer immunotherapy, incorporating engineered antigen recognition and activation pathways that distinguish them from traditional CAR-T and CAR-NK cells. These pathways are crucial for optimizing CAR-MΦ therapies for clinical use [ 17 ].

CAR-MΦ is engineered with synthetic receptors targeting specific tumor antigens. These receptors typically include an extracellular antigen-binding domain derived from an antibody’s single-chain variable fragment (scFv) connected to intracellular signaling domains that trigger macrophage activation and effector functions upon antigen engagement [ 34 ]. The selection of signaling domains remains a subject of considerable debate as researchers seek to optimize configurations that maximize therapeutic benefits without provoking excessive inflammatory responses [ 8 , 19 , 109 ].

Phagocytosis and antigen presentation

The process of tumor cell engulfment by CAR-MΦ involves intricate biological mechanisms [ 17 , 110 ]. CAR-MΦ, equipped with engineered receptors, binds explicitly to antigens expressed on tumor cells [ 20 , 34 ]. This binding triggers phagocytic activity, leading to tumor cell internalization and degradation within phagolysosomes [ 111 ].

The role of CAR-MΦ in antigen cross-presentation to T cells is central to their functionality, bridging innate and adaptive immunity [ 112 ]. After processing, peptides derived from tumor cells are presented via MHC class I molecules, crucial for activating CD8 + cytotoxic T cells [ 113 , 114 ]. This step initiates a broader immune response, allowing T cells to recognize and destroy other tumor cells expressing the same antigens. Debates persist about its efficiency and reliability across different tumor environments [ 115 , 116 ]. Figure  3 illustrates the various mechanisms through which CAR-MΦ exert their effects within the TME, highlighting their multifaceted approach to tumor eradication [ 30 ].

Mechanisms of Action of CAR-MΦ in the TME. This figure illustrates the multifaceted mechanisms through which CAR-MΦ exert their effects within the TME: ( A ) Antigen Recognition and Activation Pathways: CAR-MΦ are equipped with engineered receptors that target specific tumor antigens and intracellular signaling domains, allowing them to switch from an M0 state to an M1 state, which is pro-inflammatory and antitumor. ( B ) TME Remodeling: CAR-MΦ can remodel the TME by releasing pro-inflammatory cytokines that activate exhausted CD8 + T cells and other innate immune cells, including NK cells, dendritic cells, eosinophils, and neutrophils. ( C ) Tumor Phagocytosis: When tumor antigens bind to the CAR receptor on the surface of CAR-MΦ, activation signals are generated, leading to tumor phagocytosis. This process includes recognition, activation, engulfment, and elimination within phagolysosomes. ( D ) Transcription Factor Activation and Cytokine Release: CAR-MΦ activation involves transcription factors like NF-kB, releasing inflammatory cytokines that can activate T cell-mediated immunity against tumors. ( E ) Infiltration of CAR-MΦ in Tumor Cells: CAR-MΦ play vital roles in the TME and, through their direct effects, efficiently eliminate tumor cells by phagocytosis and antigen presentation to CD8 + T cells, bridging innate and adaptive immunity. ( F ) Legend: The legend shows the names of immune and tumor cells

Despite advancements, gaps remain in understanding CAR-MΦ’s phagocytosis and antigen presentation [ 117 , 118 ]. Questions about the efficiency of tumor cell engulfment within an immunosuppressive TME and factors enhancing this process persist. Additionally, the effectiveness of antigen presentation varies across different patients and tumor types, raising concerns about consistency [ 119 , 120 ]. The variability of the TME significantly influences CAR-MΦ’s ability to perform effectively, necessitating strategies to overcome these challenges [ 44 , 94 ].

Addressing these gaps is crucial for CAR-MΦ therapy advancement [ 33 ]. Investigating molecular mechanisms that enhance CAR-MΦ and T-cell interactions, optimizing CAR constructs for improved antigen presentation, and devising methods to counteract TME immunosuppressive barriers are essential for future research [ 88 , 121 ]. A deeper understanding of these processes is vital for enhancing CAR-MΦ therapy design and clinical application [ 30 , 32 , 122 ].

Cytokine secretion and immune activation

CAR-MΦ impacts cancer immunotherapy by secreting key cytokines that activate and orchestrate the immune response [ 22 , 123 ]. These cytokines facilitate local and systemic anti-tumor actions, which are crucial for therapeutic success [ 45 , 88 , 124 ].

Key cytokines like IL-12, IL-23, and TNF-α are central to immune modulation [ 44 , 124 ]. IL-12 activates NK cells and drives CD4 + T cells into Th1 cells, which produce IFN-γ, critical for antitumor immunity [ 125 ]. IL-23 supports Th17 cell proliferation, which can support or suppress tumor growth depending on the context [ 126 ].

The ability of these cytokines to recruit and activate other immune cells is pivotal [ 19 , 39 , 124 ]. Chemokines such as CCL2 and CCL5 attract immune cells to the tumor site, facilitating a robust immune attack, critical for combating tumor heterogeneity and adaptive resistance mechanisms [ 13 , 127 , 128 ].

While the theoretical benefits of cytokine-mediated immune recruitment and activation are acknowledged, debates persist about optimal cytokine levels and types [ 129 , 130 ]. Excessive cytokine secretion can lead to systemic inflammation and side effects, necessitating careful modulation in CAR-MΦ design [ 107 , 131 ].

Significant gaps remain in understanding the precise mechanisms of CAR-MΦ cytokine secretion and immune response modulation [ 132 , 133 ]. Further research is needed to optimize cytokine profiles for therapeutic efficacy and safety, particularly in solid tumors [ 17 , 35 ].

Phenotypic characterization of CAR-MΦ

Phenotypic characterization of CAR-MΦ is essential to understand their transitions from an M0 (naive) state to an M1 (pro-inflammatory) or M2 (anti-inflammatory) state. The characterization involves assessing the expression of surface markers, cytokine profiles, and functional properties of the engineered macrophages. This incorporation of co-stimulatory domains such as CD28 or 4-1BB in the CAR construct is crucial for macrophage activation, survival, and functionality [ 19 ].

Tumor cell killing mechanisms

CAR-MΦ targets cancer cells through direct and indirect mechanisms, showcasing their multifaceted role in cancer therapy [ 19 , 33 , 35 , 134 ]. Directly, CAR-MΦ engages in phagocytosis, binding to tumor antigens and initiating tumor cell engulfment and degradation within phagolysosomes [ 8 , 110 ]. This direct interaction physically removes tumor cells and leads to their breakdown and destruction, a process noted for its effectiveness in eliminating tumor cells [ 20 , 135 ].

Indirectly, CAR-MΦ alters the TME through immune modulation. Secreting cytokines and presenting tumor antigens activate and recruit immune cells to the tumor site, enhancing the overall immune response [ 36 , 102 ]. This recruitment strategy is critical for immediate efficacy and sustaining long-term anti-tumor activity [ 30 , 136 ].

Despite recognized benefits, gaps remain in understanding CAR-MΦ’s capabilities [ 19 , 22 ]. Questions about phagocytic efficiency in immunosuppressive environments and optimal cytokine profiles for sustained immune responses persist [ 17 , 35 ]. Further research is required to optimize CAR-MΦ designs for consistent clinical outcomes [ 30 , 32 ].

Strategies for enhancing CAR-MΦ efficacy

Advancing through genetic engineering, researchers refine CAR constructs to improve macrophage activation specificity and durability. Innovations like switch receptors and signaling pathway modifications fine-tune anti-tumor effects and control immune responses [ 137 , 138 ]. These advancements aim to amplify CAR-MΦ’s capabilities while managing off-target effects and systemic toxicity [ 139 , 140 ].

Exploring combination therapies adds complexity and promise. CAR-MΦ is used alongside other immunotherapeutic agents, like checkpoint inhibitors, designed to overcome TME immunosuppressive barriers and enhance immune response [ 19 , 30 , 34 , 136 ]. Combining CAR-MΦ with traditional treatments like chemotherapy and radiation aims to reduce tumor burden and modify the TME for more effective CAR-MΦ activity [ 36 , 88 , 141 ].

Debates continue over the best combination methods, treatment timings, and managing compounded side effects [ 142 , 143 ]. Substantial gaps remain in understanding the long-term efficacy and safety of these strategies, their impact on patient outcomes, and optimal CAR-MΦ integration with existing treatments. Continuous innovation and rigorous clinical testing are crucial for transitioning CAR-MΦ therapies from experimental approaches to standard cancer care, enhancing direct anti-tumor activities and systemic immune responses [ 8 , 35 , 144 ].

Currently, CT-0508 is safe and feasible to manufacture. Early data demonstrate trafficking, TME modulation, and potential antitumor T cell immunity induction. The study is actively enrolling participants [ 18 ]. We look forward to the results from the ex vivo combination sub-study with pembrolizumab and the continued development of CAR-MΦ and CAR-Monocyte therapies.

Technological and manufacturing challenges

Optimization of car-mφ design.

The Optimization of CAR-MΦ design is crucial in addressing the challenge posed by the variety of expressions on tumor cells. The presence of varying levels or types of antigens among tumor cells within a tumor mass or across tumors can hinder the effectiveness of CAR-MΦ therapies if the engineered receptors target only specific antigens present in certain tumor cell subsets.

One approach to tackle variability is designing CAR-MΦ that can target multiple antigens simultaneously. By incorporating single-chain variable fragments (scFvs) into the CAR structure, these CAR-MΦ can identify and bind to various TAAs. This multi-targeting strategy increases the chances of reaching a range of tumor cells within heterogeneous tumors [ 17 , 19 ].

Another strategy involves utilizing scFvs that recognize epitopes shared by an array of tumor cells. These reactive scFvs are created to bind to antigens found across different types of tumors, thereby enhancing the overall effectiveness of CAR Macrophage therapy against heterogeneous tumors [ 122 ].

Furthermore, CAR-MΦ can be designed with signaling domains that allow them to adjust their response according to the specific conditions in the TME. For example, including stimulatory molecules, like CD28 or 4-1BB, in CAR design improves macrophage survival, growth, and ability to engulf particles even when encountering different antigen expression levels [ 8 ].

Pairing CAR-MΦ therapy with treatments such as checkpoint inhibitors or traditional chemotherapies can tackle the challenge of antigens. By disrupting the immune-suppressing tumor microenvironment and reducing the diversity of tumor cells, these combinations can boost CAR-MΦ effectiveness in targeting a range of tumor cell populations [ 30 , 34 ].

Moreover, recent progress in epigenetic alterations allows for the modification of tumor cells to display antigens. Techniques like CRISPR/Cas9 can modify tumor cell genomes to make them more identifiable, to CAR-MΦ by standardizing antigen expression throughout the tumor mass [ 145 ].

Figure 4 provides an overview of the steps involved in optimizing CAR-MΦ design, including target selection, CAR construction design, and co-stimulatory domain activation pathways.

Optimization of MΦ Design. ( A ) Target Selection: CAR-MΦ is engineered to target specific tumor-associated antigens. The ectodomain of the CAR is designed to recognize these tumor antigens, ensuring precise targeting and engagement with tumor cells. ( B ) CAR Construction Design: Constructing CAR- MΦ involves inserting genes for the CAR into macrophage cells. The CAR structure includes an antigen recognition domain, a spacer, transmembrane domains, co-stimulatory domains (e.g., CD28 or 4-1BB), and CD3 essential signaling domains. These components are crucial for the activation and function of CAR-MΦ. ( C ) Co-stimulatory Domain and Activation Pathway: Upon activation by IFN-γ and IPS, CAR-MΦ transitions from an M0 (naive) state to an M1 (pro-inflammatory) state. This activation leads to the secretion of inflammatory cytokines such as IL-6, IL-8, IL-12, TNF-α, and TNF-β, which are essential for enhancing the antitumor immune response

Ex vivo Manufacturing processes

The ex vivo manufacturing processes for CAR-MΦ are crucial for producing compelling and consistent therapeutic cells. Differentiating and expanding macrophages under controlled conditions involves several vital factors [ 26 ]. Initially, monocytes are isolated from PBMCs of the patient or donor. These monocytes are then cultured in the presence of specific growth factors, such as M-CSF or GM-CSF, to promote their differentiation into macrophages [ 146 ]. Careful monitoring of the culture environment, including temperature, pH, and oxygen levels, is essential to maintain cell viability and functionality [ 147 ].

Regarding sourcing macrophages, the choice between autologous and allogeneic sources remains a subject of ongoing debate. Autologous macrophages, derived from a patient’s cells, are favored for their lower risk of eliciting an immune response. Yet, their use is hindered by variability in cell quality and scalability challenges [ 148 , 149 ]. Conversely, allogeneic macrophages, sourced from donors, offer advantages in scalability and consistency but come with an increased risk of immune rejection and complications like GVHD [ 19 , 38 ].

Transduction with viral vectors encoding the CAR construct ensures stable expression of CAR on the macrophage surface. The transduction efficiency and expression levels are rigorously evaluated using flow cytometry and molecular techniques [ 17 ]. Following transduction, the CAR-MΦ is expanded in vitro under optimized conditions supporting their growth and activation, including cytokines like IL-4 and IFN-γ for a pro-inflammatory phenotype conducive to anti-tumor activity. Validating functionality involves assessing antigen recognition, phagocytic ability, and cytokine secretion profile [ 150 ].

The protocols for differentiating and expanding macrophages are equally critical. Maintaining controlled conditions promotes the differentiation of progenitor cells into macrophages and ensures these cells appropriately express CAR constructs targeting specific tumor antigens [ 151 ]. However, balancing practical CAR expression and maintaining macrophage functionalities present a considerable challenge, often leading to variability in therapeutic outcomes [ 152 ]. High levels of CAR expression may enhance antigen recognition and tumor cell killing. However, they can lead to excessive activation and cytokine release, increasing the risk of adverse effects like CRS [ 142 , 153 ]. To mitigate this, fine-tuning the transduction protocols to achieve an optimal expression level that maximizes therapeutic benefits while minimizing toxicity is necessary [ 154 ].

Quality control and standardization are pivotal for the safety and efficacy of CAR-MΦ therapies [ 155 ]. Stringent testing protocols assess the purity, potency, and identity of CAR-MΦ batches. Significant gaps exist in standardization processes, particularly concerning the long-term stability and functional consistency of CAR-MΦ post-cryopreservation, and developing universal standards applicable across different manufacturing facilities [ 156 ].

These areas of active research and debate illuminate the factors that influence the ex vivo production of CAR-MΦ. Addressing these gaps, particularly in standardizing processes and enhancing cell source viability, is crucial for advancing CAR-MΦ therapies from experimental stages to reliable clinical applications [ 39 , 157 ].

Composition of CT-0508

The CT-0508 consists of autologous macrophages genetically engineered to express a CAR that targets the HER2 expression in solid tumors. This CAR construct, in CT 0508 includes a domain with a scFv, which is specific to the HER2 antigen, and inner signaling domains like CD28 and CD3ζ that are essential for activating, sustaining, and enhancing the macrophage’s functions [ 141 ].

To genetically modify the macrophages, a viral vector is employed to insert the CAR gene into their makeup to ensure its presence on the cell surface. These elements showcase the engineering involved in CT 0508 to enhance the accuracy and efficacy of CAR MΦ therapy, for treating HER2 positive cancers [ 120 ].

In vivo reprogramming approaches

The exploration of in vivo reprogramming approaches for CAR-MΦ centers on the advancements and challenges associated with nanoparticle-mediated delivery, as well as viral and non-viral gene editing techniques [ 153 ].

Nanoparticle-mediated delivery is emerging as a promising method for the targeted transformation of macrophages into CAR-MΦ directly within the patient’s body [ 158 ]. This technique leverages the unique capabilities of nanoparticles to deliver genetic materials or modulatory substances, especially to macrophages at tumor sites [ 159 ]. The precision of this method aims to enhance CAR constructs’ integration and functional efficacy in vivo [ 160 ]. However, there remains a debate over the consistency and safety of nanoparticle delivery, with concerns about off-target effects and the long-term viability of reprogrammed macrophages [ 161 , 162 ].

Regarding gene editing, viral vectors such as lentiviruses and adenoviruses have demonstrated high efficiency in gene delivery and are widely utilized despite potential risks such as insertional mutagenesis and eliciting immune responses [ 163 , 164 ].

Due to these risks, the field is somewhat divided on the reliance on viral vectors [ 165 ]. In contrast, non-viral methods like CRISPR-Cas9 and transcription activator-like effector nucleases (TALENs) offer a safer alternative, minimizing risks of genomic alterations and adverse immune reactions [ 166 ]. These non-viral techniques provide precise editing tools that can enhance the specificity of CAR-MΦ therapy. However, their efficiency and the durability of gene edits in clinical settings continue to be areas of intense investigation [ 167 ].

The literature reflects broad consensus on the potential of these in vivo reprogramming approaches to revolutionize CAR-MΦ therapies by improving their adaptability and patient-specific efficacy [ 39 ]. However, significant gaps in knowledge exist, particularly concerning the long-term effects of in vivo reprogrammed CAR-MΦ, the control of gene editing tools within complex tumor environments, and the overall safety of these interventions [ 8 , 30 , 39 ]. Further research is needed to address these challenges, aiming to refine these techniques for safer and more effective clinical applications.

Cost and scalability issues

The transition of CAR-MΦ therapies from experimental to widely available treatments hinges significantly on resolving cost and scalability issues [ 168 ]. Current knowledge indicates that the high manufacturing costs stem from intricate cell engineering, complex culture conditions, and the necessity for stringent quality control, which drive up production expenses [ 169 ]. Efforts to address these costs focus on refining manufacturing techniques to enhance the efficiency of cell expansion and gene editing, which could substantially reduce costs.

However, considerable debate remains over the best methods to scale production without compromising the quality and efficacy of CAR-MΦ therapies [ 170 ]. Some consensus exists around the potential of automated bioreactors and closed-system cell culture technologies, which promise to increase production capacity and reduce labor costs and contamination risks [ 34 , 171 ].

Despite these advancements, significant gaps in our understanding of scalable CAR-MΦ production persist [ 32 ]. Questions about best standardizing production protocols to ensure consistent quality across different manufacturing sites are still unresolved. Furthermore, the economic viability of scaling up CAR-MΦ therapies to meet global demand, particularly for widespread diseases like cancer, remains a contentious issue [ 32 , 172 ]. Additional research and development are needed to create cost-effective, scalable manufacturing solutions to support the widespread clinical use of CAR-MΦ therapies.

Regulatory and ethical issues

Regulatory pathways for car-mφ approval.

The regulatory approval process for CAR-MΦ is an evolving area that reflects the complexities inherent in bringing new cellular therapies to market [ 8 , 20 ]. While regulatory frameworks for CAR-T cell therapies provide a foundation, the unique properties of CAR-MΦ necessitate specific considerations. These include their multifunctional role in immune modulation and tissue repair, which could have different implications for patient safety and therapeutic outcomes [ 30 , 173 ].

Comparatively, the regulatory journey for CAR-T cells has established a precedent that emphasizes stringent evaluation of safety and efficacy. However, CAR-MΦ therapies introduce new variables, such as their phagocytic nature and the broad spectrum of cytokine production, which can affect both tumor and non-tumor tissues [ 158 ]. This raises debates about the adequacy of existing regulatory pathways to fully address the nuanced risks associated with macrophage-based therapies.

Controversies emerge particularly around the long-term effects of CAR-MΦ, given their potential to extensively alter immune system dynamics [ 30 , 36 ]. Regulatory bodies are challenged to develop guidelines that adequately address these concerns while fostering the innovation necessary to realize CAR-MΦ’s therapeutic potential [ 174 ]. There is consensus on the need for tailored regulatory approaches that consider the unique biological behaviors of macrophages and their interaction with the TME.

However, significant gaps in knowledge persist, especially regarding the long-term safety and behavior of genetically modified macrophages in humans. These gaps highlight the need for comprehensive preclinical and clinical data to inform regulatory decisions, ensuring that CAR-MΦ therapies are both practical and safe for patients. This section delves into the current state of regulatory processes, emphasizing the ongoing dialogue between researchers, regulators, and the biopharmaceutical industry to refine the approval pathways for these promising but complex therapies.

Safety monitoring and reporting

Safety monitoring and reporting for CAR-MΦ therapies are critical components of their clinical development, given the significant potential for adverse effects such as CRS and other immune-related events. Current frameworks for managing these risks involve protocols adapted from CAR-T cell therapies but tailored to address the unique properties of macrophages. The protocols emphasize early detection and intervention to mitigate the severity of CRS, which remains a primary concern with all CAR therapies [ 87 , 175 ].

There is a consensus on the need for robust, long-term follow-up to monitor the late-onset effects of CAR-MΦ treatments, which are not fully understood due to these therapies’ novel mechanisms of action [ 176 ]. The long-term safety profile is especially pertinent given the CAR-MΦ’s ability to alter the TME and potentially affect the immune system in unforeseen ways.

Debates continue over the best practices for safety monitoring, particularly concerning the balance between thorough data collection and the practicality of long-term follow-up in a clinical setting [ 5 , 177 ]. Questions also persist about the sufficiency of current adverse event reporting systems and whether they adequately capture the range of possible complications, particularly those unique to macrophage-based therapies [ 160 ].

Significant gaps in knowledge remain, particularly in how CAR-MΦ interacts with diverse patient immunology over extended periods [ 142 ]. Further research is needed to develop and standardize safety monitoring protocols that can effectively track and manage the complex safety profile of CAR-MΦ therapies [ 178 ]. These efforts are crucial for ensuring patient safety and facilitating the broader adoption of this promising therapeutic approach in oncology.

Ethical considerations

The integration of CAR-MΦ therapies into clinical practice brings forth complex ethical considerations, particularly regarding patient selection, informed consent, and equitable distribution of these emerging treatments [ 179 ]. The current discussion focuses on ensuring ethical standards in patient selection by establishing scientifically valid and morally sound criteria, aiming to balance the potential benefits and risks associated with CAR-MΦ therapies effectively [ 180 ]. The informed consent process is critical, as it must fully educate patients about the experimental nature of CAR-MΦ, potential risks, expected benefits, and possible side effects to ensure decisions are made with adequate knowledge and free of coercion [ 181 ].

Debates around access and equity are particularly vigorous, reflecting broader concerns about the availability of cutting-edge medical treatments. There is consensus on the need for strategies to prevent socioeconomic status or geographic location from limiting access to these therapies. However, there is controversy over how best to implement such strategies effectively and relatively [ 182 ]. The literature highlights a significant gap in frameworks that could guide equitable access, suggesting that international collaboration is needed to develop policies that facilitate broad and fair distribution without compromising the quality of care.

The ethical implications of CAR-MΦ therapies also extend to long-term societal impacts, such as the potential for altering healthcare paradigms and patient expectations. Current ethical discussions often do not fully address the long-term consequences of widespread CAR-MΦ adoption, indicating a critical area for future research and policy development [ 183 ]. As CAR-MΦ technologies advance, ongoing ethical scrutiny will be essential to navigate the complexities of introducing these innovative therapies into routine clinical settings, ensuring they benefit all patients regardless of their background.

Conclusion and future perspectives

This review has critically analyzed the evolving field of CAR-MΦ therapies, identifying groundbreaking advancements and persistent challenges in their development. The synthesis of current research underscores CAR-MΦ as a pioneering approach within cancer immunotherapy, particularly for solid tumors where conventional CAR-T therapies face limitations. Key findings reveal that while CAR-MΦ demonstrates significant potential in modulating the TME and enhancing immune responses, there are substantial gaps in optimizing CAR constructs for maximum specificity and efficacy [ 35 ].

Debate continues over the best strategies for CAR-MΦ deployment, with discussions centering on the balance between potent anti-tumor actions and controlling systemic immune reactions to prevent adverse effects. The literature reflects a consensus on the innovative capacity of CAR-MΦ to transform cancer treatment. Yet, it also highlights controversies regarding their long-term efficacy and safety, which remain inadequately explored in diverse clinical settings [ 32 , 136 ].

Future research should address these gaps by refining genetic engineering techniques to enhance the precision and stability of CAR-MΦ activation [ 144 ]. Expanding clinical trials to include more comprehensive range of tumor types and patient demographics is crucial for understanding the broader applicability of CAR-MΦ therapies [ 35 ]. Additionally, ethical considerations regarding patient selection and access to these emerging therapies need a thorough examination to ensure equitable treatment across different populations [ 183 ]. By continuing to explore these areas, the field can move towards fully integrating CAR-MΦ into the next generation of standard cancer care, potentially revolutionizing outcomes for patients with previously resistant forms of cancer [ 19 ].

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

adoptive cell transfer

antibody-dependent cellular cytotoxicity

acute lymphoblastic leukemia

chimeric antigen receptor

CAR Macrophage

CAR natural killer cell

cytokine release syndrome

diffuse large B-cell lymphoma

human epidermal growth factor receptor 2

hemophagocytic lymphohistiocytosis

immune effector cell-associated neurotoxicity syndrome

Interferon gamma

immune checkpoint inhibitor

interleukin

macrophage activation syndrome

natural killer

transcription activator-like effector nucleases

tumor-associated macrophages

tumor microenvironment

tumor necrosis factor-alpha

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Acknowledgements

The authors would like to thank Professor Qingqing Wang at Zhejiang University for her meticulous review and edits of this manuscript. The figures were created using BioRender.

This work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province, China (2020Y9097), and the Fujian Provincial Health Technology Project, China (2021GGA019) awarded to P.C.

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All the authors collectively conceived and designed this comprehensive review. J.L. and P.C. conducted the literature search and drafted the initial manuscript. W.M. provided supervision, graphics support, editing, and finalized the manuscript. All authors actively participated in the revision of the manuscript, carefully reviewed it, and approved the final version for submission.

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Li, J., Chen, P. & Ma, W. The next frontier in immunotherapy: potential and challenges of CAR-macrophages. Exp Hematol Oncol 13 , 76 (2024). https://doi.org/10.1186/s40164-024-00549-9

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DOI : https://doi.org/10.1186/s40164-024-00549-9

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• Published: 12 August 2024

Detection and management of localized prostate cancer in Nigeria: barriers and facilitators according to patients, caregivers and healthcare providers

• Musliu Adetola Tolani 1 , 6 ,
• Christian A. Agbo 2 ,
• Alan Paciorek 3 ,
• Shehu S. Umar 1 ,
• Rufus W. Ojewola 4 ,
• Faruk Mohammed 1 ,
• Ernie Kaninjing 5 ,
• Muhammed Ahmed 1 &
• Rebecca DeBoer 3  

BMC Health Services Research volume  24 , Article number:  918 ( 2024 ) Cite this article

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Prostate cancer mortality rates are high in Nigeria. While prostate cancer is highly curable with early detection and effective multidisciplinary management, the quality of care is suboptimal in this setting. Sustainable delivery of high-quality care for patients with localized prostate cancer is needed to save more lives. To inform future interventions to improve care, this study aimed to identify barriers and facilitators that influence prostate cancer detection and management in Nigeria.

Six focus group discussions (FGDs), stratified by stakeholders were conducted with a purposive sample of prostate cancer patients ( n  = 19), caregivers ( n  = 15), and healthcare providers ( n  = 18), in two academic tertiary hospitals in northern and southern Nigeria. A discussion guide organized based on the socio-ecological model was used. FGDs were recorded, transcribed, and analysed using the framework technique.

Barriers and facilitators were identified at the individual, interpersonal, and organizational levels. Barriers to detection included limited knowledge and misperceptions among patients, caregivers, and community-based non-specialist healthcare providers, and limitations of centralized opportunistic screening; while facilitators included the potential for religious institutions to encourage positive health-seeking behaviour. Barriers to management included non-uniformity in clinical guideline usage, treatment abandonment amidst concerns about treatment and survival, absence of patient interaction platforms and follow-up support systems, difficulty in navigating service areas, low health insurance coverage and limited financial resource of patients. Facilitators of management included the availability of resource stratified guidelines for prostate cancer management and the availability of patient peers, caregivers, nurses, and medical social workers to provide correct medical information and support patient-centred services. Participants also provided suggestions that could help improve prostate cancer detection and management in Nigeria.

This study identified multiple determinants affecting the detection and management of localized prostate cancer. These findings will inform the refinement of implementation strategies to improve the quality of prostate cancer care in Nigeria.

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The burden of prostate cancer in Nigeria, a lower middle-income country on the west coast of Africa, is rising. Specifically, the incidence of this cancer increased from 11,944 in 2014 to 15,306 in 2020 [ 1 , 2 ]. Wide disparities in the mortality-to-incidence ratio in different geographical contexts (i.e., 0.56 in Western Africa versus 0.16 in Northern America and 0.19 in Western Europe) [ 3 ], illustrate the glaring inequity in prostate cancer management outcomes between Low and Middle-Income Countries (LMICs) and High-Income Countries (HICs).

Clinically localized prostate cancer means T1─T3a, N0,M0 disease [ 4 ]. Curative treatment, which may include active surveillance, radical prostatectomy, radical radiotherapy, and/or androgen deprivation therapy, is recommended for this early stage of cancer. However, the pathways for screening and early diagnosis, staging, treatment, and follow-up in these patients are complex, involving all tiers of the healthcare system and multidisciplinary specialists. A National Cancer Control Plan (2018–2022) was published as a framework to address gaps in priority areas for cancer detection and management in Nigeria [ 5 ]. However, the aspects of community-based approaches to detection, guideline use, treatment access, and survivorship care, remain largely unimplemented during the delivery of care to these patients. Reduction in prostate cancer mortality rates will require increased attention to the detection and cure of early-stage disease. Although some researches have been conducted to characterize the barriers and facilitators of common cancers, like breast and cervical cancers, in Africa; there is paucity of research to comprehensively study the determinants of prostate cancer detection and management in this geographical context. The characteristics of prostate cancer patients are quite unique and different from those of these cancers; hence the need to holistically study these determinants.

Interventions are also needed to sustainably strengthen the care delivery system for localized prostate cancer in Nigeria. Needs assessment represents an important phase of intervention development by creating a better understanding of contextual factors that have significant potential to influence the implementation of tailored pilot interventions and scale-up of future programs. To inform the future development of interventions to improve care, this study aimed to identify barriers and facilitators that influence the detection and management of clinically localized prostate cancer in the country.

Nigeria is the largest country in Africa. The study was carried out at two public tertiary hospitals and designated comprehensive cancer care centres in different geopolitical zones: Ahmadu Bello University Teaching Hospital, a 730-bed hospital in Zaria in the Northwest Zone, and Lagos University Teaching Hospital, a 950-bed hospital in Lagos in the Southwest Zone. These facilities include prostate cancer care as part of the oncology services provided. Diagnostic services include core biopsy of the prostate with histopathology; laboratory services; and computed tomography (CT) and magnetic resonance imaging. Treatment services include radical prostatectomy, external beam radiotherapy with linear accelerator or cobalt-60, brachytherapy, and androgen deprivation therapy with GnRH agonists. Payment for clinical care is predominantly through a fee-for-service model. These centres receive referrals from primary and secondary healthcare centres where the delivery of preventive oncology services is not structured.

Study design

This qualitative research, with a phenomenological study design, was conducted from August to September 2022. It utilized focus group discussions. Focus groups provided an avenue for researchers to gain an in-depth understanding of participants’ experiences regarding prostate cancer detection and management in Nigeria. Focus groups also provided an opportunity to capture the context in which the health behaviour occurred, and how treatment and cure were provided. These are important topical areas given the dearth of research among this population. Socio-ecological model was used as the theoretical framework for this study as its levels has important determinants and can facilitate the design of a comprehensive intervention that holistically addresses various dimensions of the barriers and facilitators [ 6 ].

Sampling, participant eligibility, and recruitment

Purposive sampling was used to select a diverse group of participants [ 7 ]. Eligible patients included those who were diagnosed with clinically localized prostate cancer from all risk groups of prostate cancer and have been managed at the study site for a period of not less than 6 months. Eligible caregivers included family caregivers and informal caregivers such as neighbours and friends above the age of 18 years, and professional caregivers such as medical social workers, who must have been present in a minimum of one prostate cancer-related hospital visit at the site to offer healthcare support to study-eligible patients. Eligible healthcare providers included those with at least 3 years of experience in the management of prostate cancer in the specialties of urology, radiology, pathology, clinical oncology, radiation oncology and oncology nursing. The patients and caregivers were identified during prostate cancer clinic visits while the staff list at the study site was used to identify the healthcare providers. These participants were recruited through in-person or email invitation four weeks before the FGD session, and their written informed consent was obtained.

Data collection

Study participants were stratified into three groups: patients , caregivers , and healthcare providers . Informed consent was obtained from all participants. A short demographic survey, to capture the profile of study participants, in terms of age, gender, geographic region, educational attainment, and employment status, was completed prior to each focus group. Participant characteristics are summarized on Table  1 .

Six focus groups sessions were held, two with patients, two with caregivers, and two with healthcare providers. The focus groups were conducted in-person at the two study sites in a location that allowed for privacy and maintenance of confidentiality. A semi structured discussion guide was developed. It focused on three domains of influence from the socio-ecological model (individual, interpersonal, and organizational) [ 6 ], to elucidate participants’ experiences and opinions related to barriers and facilitators of prostate cancer care. Sample questions of the discussion guide are presented on Table  2 .

The moderator for the sessions, one at each site, were researchers with clinical experience, trained in the facilitation of FGDs, communicates fluently in the common language of the site, and understands the relevant clinical and environmental context of the area. The moderator welcomed the views and engagement of each participant and took time to repeat what was said in the local language used by participants to ensure that the points that the participants’ expressed were correctly noted. The discussions were audio recorded, transcribed, and then de-identified to maintain anonymity. Saturation was achieved during the sessions. The mean duration of the focus group discussions was 125 min (range, 110–141 min).

Data analysis

Qualitative analysis, using a hybrid deductive and inductive approach, was done using NVivo version 12 (QSR International Pty Ltd., Burlington, Massachusetts) based on the framework method of thematic analysis [ 8 ]. A codebook was developed based on the socioecological model, other a priori concepts in the discussion guide, and emergent themes that arose during the initial open coding of the transcripts. Based on this codebook, two investigators with different areas of expertise individually coded each transcript. Intercoder reliability was high, with an average agreement of 98.7%. Themes were mapped onto the individual, interpersonal, and organizational levels of the overarching socioecological model. The trustworthiness strategy of this study involved the iterative discussion of findings and their meanings among the research team during fortnightly meeting sessions.

Overall, participants spoke more about factors within the individual and interpersonal levels of the socioecological framework compared to organizational level factors. They identified similar numbers of barriers and facilitators, which were often related and led to recommended solutions. Summaries of the main themes are presented on Figs.  1 , 2 and 3 .

Barriers and facilitators to localized prostate cancer detection and management at the individual level

Barriers and facilitators to localized prostate cancer detection and management at the interpersonal level

Barriers and facilitators to localized prostate cancer detection and management at the organizational level

Detection of prostate cancer

Individual level, knowledge and understanding of prostate cancer.

Knowledge gaps in prostate cancer detection were mentioned more frequently among patients. One of the patients (male, 65 years) declared: “I did not know the symptoms and signs to look out for in prostate cancer” . Family caregivers, however, focused more on their lack of knowledge about Prostate Specific Antigen screening as a preventive strategy. Patients, caregivers, and providers cited instances of misinformation, low index of suspicion, and inappropriate clinical tests as barriers among untrained health practitioners in the community setting. A urology specialist (male, 52 years) stated: “When they go to other medical personnel instead of trained specialists for the disease , proper assessment is not made to identify prostate cancer” . One of the patients (male, 66 years) suggested awareness and advocacy to address this problem noting that: “There is a lot of awareness about diabetes and hypertension. Doctors should try to make more noise about PSA screening too” .

Beliefs about prostate cancer

Widespread negative beliefs about the cause of prostate cancer, such as spiritual afflictions, were commonly discussed among the patient and caregiver groups in Nigeria, contributing to the non-acceptance of diagnosis. Participants in all categories described negative perceptions of the disease that led to denial or apathy towards diagnosis. For example, a patient (male, 72 years) asked: “Why should you say that I have prostate cancer when I am asymptomatic” , and a urology specialist (male, 38 years) made reference to the widely circulated notion that “cancer is a death sentence” . However, participants in all stakeholder categories noted that religious tenets also admonish seeking appropriate care.

Interpersonal level

Interaction of patients with caregivers and wider societal contacts.

All groups articulated the positive yield of communication between patients and family and medically informed societal contacts in religious settings. For example, a family caregiver (female, 30 years) noted: “I was also talking to him to be his comfort and everything” . Caregivers placed particular emphasis on their role in the patient’s cancer journey before early detection and referenced benefits of family and community support such as championing pre-screening health education and advocacy. Another family caregiver, an elderly educationist (female, 75 years), narrated: “We tell any man above 40 years that comes to our house to go and check their prostate” .

Provider-to-provider communication for upward referrals

Patients indicated that there was no public database of prostate cancer specialists to facilitate the connection of peripheral healthcare providers to the specialists. One of the patients (male, 72 years) stated: “The data on the professionals that are here is not widespread enough for non-specialist doctors to easily check to know about which doctors are available for the management of a disease” . A urology specialist (male, 45 years), therefore, advocated for the prioritization of follow-up during the process of early diagnosis in order to address this problem. Another provider, an oncology specialist (male, 50 years) further suggested, “Our religious organizations have a lot of roles to play in supporting referral of those with certain complaints or those that are diagnosed to the appropriate doctor” .

Organizational level

Screening practice.

Healthcare providers explained that the curative management of prostate cancer was hampered by non-deliberate and suboptimal screening for localized disease. A pathology specialist (male, 36 years) cited the incidental nature of most localized diagnoses while a urology specialist (male, 45 years) highlighted the limitations in the reach of opportunistic screening presently conducted by specialists as “we cannot get the number that we need to improve early prostate cancer diagnosis especially because majority of patients do not present with symptoms” . Another urology specialist (male, 42 years) explained that this barrier results from the limited culture of routine medical check-ups for health maintenance and suggested that to close this gap, “Community based screening is where we need to look towards” . All stakeholder categories further pointed out that low health insurance coverage and poor financial resource of patients was a barrier to screening and diagnosis. As a consequence of this challenge, an oncology nurse (female, 30 years) noted that: “People will not want to come to the hospital” .

A summary of the suggestions for improvement in prostate cancer detection is presented on Table  3 .

Management of prostate cancer

Treatment attitude, decisions, and adherence.

Discussion about facilitators of treatment adherence was predominant in all groups. A medical social worker (male, 32 years) cited an example of his re-enforcement of treatment recommendations through interaction with a patient, while an oncology specialist (male, 50 years) suggested regular follow-up reminders to strengthen patients’ treatment adherence.

Nevertheless, participants also reported negative attitudes toward treatment. Patients (males, 74 and 76 years) cited concerns about radical treatment in the young and surgical fitness in the older adults while an oncology specialist (female, 40 years) emphasized the aversion of patients “when they hear of the risk of erectile dysfunction” as a barrier to optimal treatment decisions and adherence. Other patients (males, 70 and 74 years) noted that being scared was a detriment to optimal treatment while caregivers (females, 57 and 65 years) further described uncooperative attitudes among patients. This is sometimes because “ they are in denial of the diagnosis ” (urology specialist, male, 42 years).

Emotional state

Overall, there were fewer discussions related to the emotional impact of prostate cancer. One patient (male, 56 years) noted that following diagnosis, “I felt that everything in my life was gone” , whereas other patients expressed positive emotions. Many caregivers expressed, in strong emotional tones, a feeling of confusion and sadness due to the psychologically overwhelming burden. For example, one of the family caregivers (female, 30 years) said: “It was not easy emotionally because my mum is late. I cried so much in the bathroom”. Patients and caregivers reflected that these negative states could adversely affect the motivation of patients to seek health care. A patient (male, 56 years) however noted that psychological counselling supported the strengthening of resilience in surmounting the distress when he was “losing control of everything”.

Provider use of clinical guidelines and protocols

Healthcare providers acknowledged the availability of international resource-stratified consensus guidelines for prostate cancer management but described practical examples pointing to low levels of adherence to guidelines and non-uniformity in the source document used. A urology specialist (male, 42 years) cited that “ most urologists in Nigeria tend to use the European Association of Urology guidelines ” in contrast to an oncology specialist (male, 40 years) who noted that “ If there are any guideline that I really use as an oncologist , it is the National Comprehensive Cancer Network guideline ”. This led to a significant variation in treatment recommendations among providers (oncology specialist, male, 63 years). The oncology specialist (male, 40 years) therefore stressed the need for institutional support to strengthen guideline adherence. A pathology specialist (male, 36 years) further highlighted the need for more locally generated evidence by “ communicating with the department and people involved in order to identify and consider available resource and personnel peculiarities ”.

Patient-to-patient communication

Participants placed greater emphasis on barriers to patient-to-patient communication than facilitators. Patients unanimously highlighted the absence of platforms to support interaction among patients. One patient (male, 68 years) noted: “Everybody is just on their own” . However, patients referenced models, such as meetings among a military veteran group (male, 70 years) and face-to-face discussion on clinic sidelines (males, 56 and 74 years), as successful facilitators of peer interaction. A urology specialist (male, 42 years) therefore advocated for the creation of a formal patient support group “ where patients can exchange experiences and see that what the doctor is saying is actually true ”.

Interaction of patients with caregivers and medical social workers

All groups articulated the positive yield of communication between patients and family caregivers or medical social workers. Caregivers discussed their positive role in providing more context about the patient during clinic consultation and in the delivery of psychosocial support. For example, one of them (female, 66 years) said: “I told him that I’ll go through it with him , and we are going to see it to the end together. That assurance created some relief for him”. A patient (male, 69 years) also referenced benefits such as getting timely medication reminders from caregivers.

Provider-to-patient communication

Provider-to-patient communication was the most dominant facilitator discussed across the three stakeholder categories. Patients described the value of face-to-face discussions (male 74 years), short messaging service (male, 62 years), and printed documents (males, 56 and 69 years). Caregivers (females, 66 and 65 years) cited the importance of phone calls as channels of communication between providers and patients. An oncology provider (female, 40 years) highlighted the advantage of this information exchange noting that: “you can actually tell them that their case is curable. It makes them relaxed and happy” . However, other providers such as the urology specialist (male, 42 years) noted the hurdle of lack of interpreters while the pathology specialist (male, 60 years) reflected that providers have communication skill deficits and suggested implementing communication training for providers. Patients further made reference to time limitations during deep conversations with doctors, variable access to education materials, and difficulty in obtaining timely response to information needs. One of the patients (male, 56 years) identified opportunities for ancillary staff, such as nurses to facilitate information provision. A medical social worker (male, 32 years) reiterated: “ Many patients come to seek advice. I usually go back to the managing clinician to get further clarity on the patient. I then advice and counsel the patients ”.

Provider-to-provider communication in multidisciplinary teams

Healthcare providers emphasized that tumour boards were the fulcrum of provider-to-provider communication. A urology specialist (male, 52 years) explained that tumour boards provided an opportunity to brainstorm on the best clinical management option for patients and fast-track the decision-making process while stating that it was difficult to achieve complete attendance due to the conflicting activities of providers. An oncology specialist (male, 33 years) further indicated that the communication was not optimal because of the tight schedule of providers.

Process of care

Patients and caregivers strongly articulated several shortcomings during the process of their care citing examples of inefficient clinic appointment systems characterized by long waiting times, delays in the release of test results, and the significant difficulty and loss of energy experienced by sick patients while navigating the vast hospital complex to retrieve hospital cards, make payments, and submit blood and tissue samples. As a result of the long waiting times, a caregiver (female, 57 years) explained, “Many people don’t want to come to the hospitals , especially these big ones” . Patients and caregivers further identified public transportation through relatively long distances to the hospital as another disincentive to achieving optimal care. A patient (male, 56 years) therefore advocated for the presence of navigators who will direct and support patients during the transition between points of care.

Healthcare financing of treatment

All stakeholder categories also discussed the significant limitation posed by the low health insurance coverage and financial resources of patients to treatment services. One of the patients (male, 66 years) attributed this to their vulnerable status as senior citizens on little or no pension schemes, while an oncology nurse (female, 30 years) ascribed it to the exclusion of most oncology-directed treatment in the popular public health insurance scheme.

Other themes at the organizational level

Patients and providers emphasized several deficits in “equipment availability and function” including geographical disparity in access to cancer care facilities in northern Nigeria that pose serious challenges to timely care. Patients (males, 59 and 66 years) also saliently noted the limited number of oncologists. An oncology specialist (male, 63 years) also highlighted the limited number of psychologists who were considered important in helping patients cope with “a tumour that can affect patients’ sexual activity” . A urology specialist (male, 38 years) thus suggested the introduction of basic psychological screening by oncologists to “give hope to the patients”. Finally, the need for “Data registry and clinicopathological documentation” also emerged as a theme at this level.

A summary of suggestions for improvement in prostate cancer management is presented on Table  4 .

Improvement in the pathways to detection and management is vital to improvement in survival outcomes of cancers [ 9 ]. Interventions designed to improve these pathways should be based on stakeholder input. This study therefore explored the unique viewpoints of patients, caregivers, and providers on the determinants of successful detection and management of clinically localized prostate cancer at two designated comprehensive cancer centres in Nigeria.

Regarding prostate cancer detection, the finding that determinants at the individual level of the socioecological model were more dominant reflects a major need for improvement in patients’ awareness of prostate cancer and in their help-seeking behaviour. Gaps in knowledge and understanding and the negative beliefs of patients and caregivers about prostate cancer suggest that there is a low level of prostate cancer health literacy among patients and caregivers. Similar to this study, Kaninjing at al. and Ezenwankwo et al. have reported poor awareness of prostate cancer and its red-flag symptoms and disease misattribution due to reliance on folklore and myths as barriers to care-seeking in the Nigerian context [ 10 , 11 ]. This study further linked knowledge barriers among healthcare providers at the primary and secondary levels of care to timely diagnosis, therefore, providing a potentially actionable target for improvement in prostate cancer early detection in Nigeria.

Another challenge of prostate cancer early detection highlighted in this study is the relative centralization in the provision of cancer assessment at tertiary levels of care in Nigeria. Community stakeholders, in the study of Adedeji et al. [ 12 ], noted that the absence of prostate cancer screening centres, across the local government areas that serve rural dwellers, was a barrier to the access of this preventive care service. The perspective of providers in this study, therefore, reinforces the opinion of community stakeholders in their study. Moreover, this study further describes the difficulties in the referral of patients from community-level healthcare centres to specialist hospitals. These organizational and interpersonal-level gaps has a negative impact on easy and early access to prostate cancer detection in the communities [ 13 ].

Turning to prostate cancer management, this study highlighted the emotional, logistical, and financial difficulties faced by patients during the processes of care. The distress that they encounter during the journey through laboratory tests, imaging investigations, radical prostatectomy, radical radiotherapy, and survivourship can adversely affect patient motivation to continue receiving care. Despite these challenges, the great dominance of interpersonal-level facilitators, such as the role of peer support among patients, family support, nurses, and medical social workers in assisting patients to overcome barriers to care and get the support needed, stood out as a finding in this study. Kim et al. in South Africa also observed the pains faced by patients living with prostate cancer and the role of social and emotional support in fostering coping and resilience [ 14 ]. These mechanisms can be leveraged as an asset to improve the quality of patient-centred care during the management of prostate cancer in Nigeria.

At the organizational level of prostate cancer management, limited provider capacity for psychological care represents a salient and important need, especially because it closely relates to the negative emotional state of some individual patients during their post-diagnosis journey. Unlike this study, where distress was related to peri-diagnosis shock and the prospect of erectile dysfunction, Kim et al. [ 14 ] identified stigma as a key challenge encountered by prostate cancer patients. It, therefore, appears that the setting of South Africa, where cancer and HIV are regarded as secret conditions, is different from the cultural context of Nigeria. It could also be that the risk of stigma is mitigated by the curative focus for prostate cancer in this present study. Participants in this study also identified other challenges at the organizational level, such as long travel distances, equipment breakdown, and inadequate health insurance programs, which have been documented as general barriers of access to cancer care in other studies in Nigeria [ 13 , 15 , 16 , 17 ].

The result of this needs assessment is important during the process of intervention development in mapping the contextual barriers and facilitators identified by stakeholders to the potential steps that can be taken to address the problems [ 18 ]. The study participants recommended solutions in the areas of prostate cancer early detection, patient navigation, guideline-based management, and basic psychological care. These represents expected changes in behaviour and environment that will be mapped to determinants, and used during brainstorming and prioritization sessions to develop a final list of systems strengthening intervention strategies for localized prostate cancer detection and management in Nigeria.

This study should be interpreted within the lens of some limitations. Because the stakeholders were purposively selected, their views may not be generalizable to the population. In addition, the opinions of community and religious leaders as well as those of policymakers were not included in this study. Nevertheless, the strength of this study lies in the use of a multi-level socio-ecological approach to deeply understand barriers and enhancers of care in a complex setting.

Conclusions

This study identified multi-level determinants that may affect the optimal diagnosis, treatment, follow-up, survivourship and secondary prevention of localized prostate cancer in Nigeria. Stakeholder priorities in the areas of early detection, patient navigation, guideline-based management and basic psychological care are recommended as targets of future interventions. This study will be used to inform implementation research on the development of these multi-faceted implementation strategies in order to improve the quality of prostate cancer detection and management in Nigeria.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The UCSF HDFCCC Global Cancer Program provided statistical support for this research and Kaitlin Vanvoorhis made the graphic designs.

This study received funding from the Prostate Cancer Foundation – Pfizer Health Equity Challenge Award.

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Musliu Adetola Tolani, Shehu S. Umar, Faruk Mohammed & Muhammed Ahmed

Dalhatu Araf Specialist Hospital, Shendam Road, Lafia, Nasarawa State, Nigeria

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Alan Paciorek & Rebecca DeBoer

University of Lagos, Lagos University Teaching Hospital, Idi-Araba, Lagos State, Nigeria

Rufus W. Ojewola

Georgia College and State University, Campus Box 112, Milledgeville, GA, 31061, USA

Ernie Kaninjing

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MAT, FM, AP, EK, MA, RD were involved in the development of the project. MAT, CAA, SSU, RWO were involved in data management. MAT, CAA, SSU, RWO, FM, EK, MA, RD did the data analysis, manuscript writing, and manuscript editing. All authors read and approved the final manuscript.

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Correspondence to Musliu Adetola Tolani .

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Ethical approval was obtained from the Health Research Ethics Committees of Ahmadu Bello University Teaching Hospital (ABUTHZ/HREC/F57/2021), Dalhatu Araf Specialist Hospital (DASH/L/ADM/0166), Lagos University Teaching Hospital (ADM/DSCST/HREC/APP/4883); and the Institutional Review Boards of University of California, San Francisco (23-39299) and Georgia College and State University (17015). Written informed consent was obtained from all the participants in the study. Participation was voluntary and confidentiality was maintained throughout the study.

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Tolani, M.A., Agbo, C.A., Paciorek, A. et al. Detection and management of localized prostate cancer in Nigeria: barriers and facilitators according to patients, caregivers and healthcare providers. BMC Health Serv Res 24 , 918 (2024). https://doi.org/10.1186/s12913-024-11340-1

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Received : 29 April 2024

Accepted : 22 July 2024

Published : 12 August 2024

DOI : https://doi.org/10.1186/s12913-024-11340-1

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Why Dropping the E in DEI Is a Mistake

• Enrica N. Ruggs
• Oscar Holmes IV

The Society for Human Resource Management’s decision to remove “equity” from its DEI framework sets a dangerous precedent that flies in the face of decades of research.

The Society for Human Resource Management (SHRM) has decided to remove “equity” from its inclusion, equity, and diversity (IE&D) framework, now promoting “inclusion and diversity” (I&D) instead. This decision sets a dangerous precedent that flies in the face of decades of research about DEI in the workplace. It undermines efforts to create equitable workplaces and ignores the vital role of equity in fostering fairness and addressing systemic barriers faced by marginalized groups. Instead of scaling back their focus on equity, companies should: 1) Commit to achievable equity goals; 2) Implement and track evidence-based DEI policies and practices; and 3) Establish accountability and transparency.

Recently, the Society for Human Resource Management (SHRM), a leading voice of HR professionals, announced that it was abandoning the acronym “IE&D” — inclusion, equity, and diversity — in favor of “I&D.”

• Enrica N. Ruggs , PhD is an associate professor of management in the C. T. Bauer College of Business at the University of Houston. She is a workplace diversity scholar who conducts research on reducing discrimination and bias in organizations and improving workplace experiences for individuals with marginalized identities.
• Oscar Holmes IV , PhD, SHRM-SCP is an associate professor of management at Rutgers University-Camden and the creator and host of the podcast Diversity Matters . In his research he examines how leaders can maximize productivity and well-being by fostering more inclusive workplaces.

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