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Case Study – Methods, Examples and Guide

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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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Case Study vs. Survey

What's the difference.

Case studies and surveys are both research methods used in various fields to gather information and insights. However, they differ in their approach and purpose. A case study involves an in-depth analysis of a specific individual, group, or situation, aiming to understand the complexities and unique aspects of the subject. It often involves collecting qualitative data through interviews, observations, and document analysis. On the other hand, a survey is a structured data collection method that involves gathering information from a larger sample size through standardized questionnaires. Surveys are typically used to collect quantitative data and provide a broader perspective on a particular topic or population. While case studies provide rich and detailed information, surveys offer a more generalizable and statistical overview.

Further Detail

Introduction.

When conducting research, there are various methods available to gather data and analyze it. Two commonly used methods are case study and survey. Both approaches have their own unique attributes and can be valuable in different research contexts. In this article, we will explore the characteristics of case study and survey, highlighting their strengths and limitations.

A case study is an in-depth investigation of a particular individual, group, or phenomenon. It involves collecting detailed information about the subject of study through various sources such as interviews, observations, and document analysis. Case studies are often used in social sciences, psychology, and business research to gain a deep understanding of complex issues.

One of the key attributes of a case study is its ability to provide rich and detailed data. Researchers can gather extensive information about the subject, including their background, experiences, and perspectives. This depth of data allows for a comprehensive analysis and interpretation of the case, providing valuable insights into the phenomenon under investigation.

Furthermore, case studies are particularly useful when studying rare or unique cases. Since case studies focus on specific individuals or groups, they can shed light on situations that are not easily replicated or observed in larger populations. This makes case studies valuable in exploring complex and nuanced phenomena that may not be easily captured through other research methods.

However, it is important to note that case studies have certain limitations. Due to their in-depth nature, case studies are often time-consuming and resource-intensive. Researchers need to invest significant effort in data collection, analysis, and interpretation. Additionally, the findings of a case study may not be easily generalized to larger populations, as the focus is on a specific case rather than a representative sample.

Despite these limitations, case studies offer a unique opportunity to explore complex issues in real-life contexts. They provide a detailed understanding of individual experiences and can generate hypotheses for further research.

A survey is a research method that involves collecting data from a sample of individuals through a structured questionnaire or interview. Surveys are widely used in social sciences, market research, and public opinion studies to gather information about a larger population. They aim to provide a snapshot of people's opinions, attitudes, behaviors, or characteristics.

One of the main advantages of surveys is their ability to collect data from a large number of respondents. By reaching out to a representative sample, researchers can generalize the findings to a larger population. Surveys also allow for efficient data collection, as questionnaires can be distributed electronically or in person, making it easier to gather a wide range of responses in a relatively short period.

Moreover, surveys offer a structured approach to data collection, ensuring consistency in the questions asked and the response options provided. This allows for easy comparison and analysis of the data, making surveys suitable for quantitative research. Surveys can also be conducted anonymously, which can encourage respondents to provide honest and unbiased answers, particularly when sensitive topics are being explored.

However, surveys also have their limitations. One of the challenges is the potential for response bias. Respondents may provide inaccurate or socially desirable answers, leading to biased results. Additionally, surveys often rely on self-reported data, which may be subject to memory recall errors or misinterpretation of questions. Researchers need to carefully design the survey instrument and consider potential biases to ensure the validity and reliability of the data collected.

Furthermore, surveys may not capture the complexity and depth of individual experiences. They provide a snapshot of people's opinions or behaviors at a specific point in time, but may not uncover the underlying reasons or motivations behind those responses. Surveys also rely on predetermined response options, limiting the range of possible answers and potentially overlooking important nuances.

Case studies and surveys are both valuable research methods, each with its own strengths and limitations. Case studies offer in-depth insights into specific cases, providing rich and detailed data. They are particularly useful for exploring complex and unique phenomena. On the other hand, surveys allow for efficient data collection from a large number of respondents, enabling generalization to larger populations. They provide structured and quantifiable data, making them suitable for statistical analysis.

Ultimately, the choice between case study and survey depends on the research objectives, the nature of the research question, and the available resources. Researchers need to carefully consider the attributes of each method and select the most appropriate approach to gather and analyze data effectively.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Using Systems Thinking for Translating Evidence into Practice: A Case Study of Embedding Shared Decision Making within a Federally Qualified Health Center Network

The following is a mixed-methods case study that examines how Access Community Health Network (ACCESS), a large federally qualified health center located in the Chicago metropolitan area, used a systems approach to incorporate Shared Decision Making into its practice model. Using both qualitative and quantitative methods including a survey of ACCESS staff and providers, as well as interviews with a range of providers and leadership, the study sought to answer the question: How successfully has ACCESS, as a complex primary care system, made Shared Decision Making an integral part of its Patient Centered Medical Home practice model?

With a high degree of consistency across both the survey and interview data, the study concludes that ACCESS has successfully shifted its culture towards Shared Decision Making and, over the course of the past several years, made it a part of its PCMH practice model. At the same time, there are still areas for improvement and ways that ACCESS can further embed SDM within its practice model. Opportunities exist to use this study as a foundation for further exploring the impact of SDM on patients and health outcomes (not a part of this study). Further, the results can be used by other complex health systems as a model for how to successfully integrate and translate best practice or innovation into care models.

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• Published: 11 May 2024

Does a perceptual gap lead to actions against digital misinformation? A third-person effect study among medical students

• Zongya Li   ORCID: orcid.org/0000-0002-4479-5971 1 &
• Jun Yan   ORCID: orcid.org/0000-0002-9539-8466 1  

BMC Public Health volume  24 , Article number:  1291 ( 2024 ) Cite this article

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We are making progress in the fight against health-related misinformation, but mass participation and active engagement are far from adequate. Focusing on pre-professional medical students with above-average medical knowledge, our study examined whether and how third-person perceptions (TPP), which hypothesize that people tend to perceive media messages as having a greater effect on others than on themselves, would motivate their actions against misinformation.

We collected the cross-sectional data through a self-administered paper-and-pencil survey of 1,500 medical students in China during April 2022.

Structural equation modeling (SEM) analysis, showed that TPP was negatively associated with medical students’ actions against digital misinformation, including rebuttal of misinformation and promotion of corrective information. However, self-efficacy and collectivism served as positive predictors of both actions. Additionally, we found professional identification failed to play a significant role in influencing TPP, while digital misinformation self-efficacy was found to broaden the third-person perceptual gap and collectivism tended to reduce the perceptual bias significantly.

Conclusions

Our study contributes both to theory and practice. It extends the third-person effect theory by moving beyond the examination of restrictive actions and toward the exploration of corrective and promotional actions in the context of misinformation., It also lends a new perspective to the current efforts to counter digital misinformation; involving pre-professionals (in this case, medical students) in the fight.

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Introduction

The widespread persistence of misinformation in the social media environment calls for effective strategies to mitigate the threat to our society [ 1 ]. Misinformation has received substantial scholarly attention in recent years [ 2 ], and solution-oriented explorations have long been a focus but the subject remains underexplored [ 3 ].

Health professionals, particularly physicians and nurses, are highly expected to play a role in the fight against misinformation as they serve as the most trusted information sources regarding medical topics [ 4 ]. However, some barriers, such as limitations regarding time and digital skills, greatly hinder their efforts to tackle misinformation on social media [ 5 ].

Medical students (i.e., college students majoring in health/medical science), in contrast to medical faculty, have a greater potential to become the major force in dealing with digital misinformation as they are not only equipped with basic medical knowledge but generally possess greater social media skills than the former generation [ 6 ]. Few studies, to our knowledge, have tried to explore the potential of these pre-professionals in tackling misinformation. Our research thus fills the gap by specifically exploring how these pre-professionals can be motivated to fight against digital health-related misinformation.

The third-person perception (TPP), which states that people tend to perceive media messages as having a greater effect on others than on themselves [ 7 ], has been found to play an important role in influencing individuals’ coping strategies related to misinformation. But empirical exploration from this line of studies has yielded contradictory results. Some studies revealed that individuals who perceived a greater negative influence of misinformation on others than on themselves were more likely to take corrective actions to debunk misinformation [ 8 ]. In contrast, some research found that stronger TPP reduced individuals’ willingness to engage in misinformation correction [ 9 , 10 ]. Such conflicting findings impel us to examine the association between the third-person perception and medical students’ corrective actions in response to misinformation, thus attempting to unveil the underlying mechanisms that promote or inhibit these pre-professionals’ engagement with misinformation.

Researchers have also identified several perceptual factors that motivate individuals’ actions against misinformation, especially efficacy-related concepts (e.g., self-efficacy and health literacy) and normative variables (e.g., subjective norms and perceived responsibility) [ 3 , 8 , 9 ]. However, most studies devote attention to the general population; little is known about whether and how these factors affect medical students’ intentions to deal with misinformation. We recruited Chinese medical students in order to study a social group that is mutually influenced by cultural norms (collectivism in Chinese society) and professional norms. Meanwhile, systematic education and training equip medical students with abundant clinical knowledge and good levels of eHealth literacy [ 5 ], which enable them to have potential efficacy in tackling misinformation. Our study thus aims to examine how medical students’ self-efficacy, cultural norms (i.e., collectivism) and professional norms (i.e., professional identification) impact their actions against misinformation.

Previous research has found self-efficacy to be a reliable moderator of optimistic bias, the tendency for individuals to consider themselves as less likely to experience negative events but more likely to experience positive events as compared to others [ 11 , 12 , 13 ]. As TPP is thought to be a product of optimistic bias, accordingly, self-efficacy should have the potential to influence the magnitude of third-person perception [ 14 , 15 ]. Meanwhile, scholars also suggest that the magnitude of TPP is influenced by social distance corollary [ 16 , 17 ]. Simply put, individuals tend to perceive those who are more socially distant from them to be more susceptible to the influence of undesirable media than those who are socially proximal [ 18 , 19 , 20 ]. From a social identity perspective, collectivism and professional identification might moderate the relative distance between oneself and others while the directions of such effects differ [ 21 , 22 ]. For example, collectivists tend to perceive a smaller social distance between self and others as “they are less likely to view themselves as distinct or unique from others” [ 23 ]. In contrast, individuals who are highly identified with their professional community (i.e., medical community) are more likely to perceive a larger social distance between in-group members (including themselves) and out-group members [ 24 ]. In this way, collectivism and professional identification might exert different effects on TPP. On this basis, this study aims to examine whether and how medical students’ perceptions of professional identity, self-efficacy and collectivism influence the magnitude of TPP and in turn influence their actions against misinformation.

Our study builds a model that reflects the theoretical linkages among self-efficacy, collectivism, professional identity, TPP, and actions against misinformation. The model, which clarifies the key antecedents of TPP and examines the mediating role of TPP, contribute to the third-person effect literature and offer practical contributions to countering digital misinformation.

Context of the study

As pre-professionals equipped with specialized knowledge and skills, medical students have been involved in efforts in health communication and promotion during the pandemic. For instance, thousands of medical students have participated in various volunteering activities in the fight against COVID-19, such as case data visualization [ 25 ], psychological counseling [ 26 ], and providing online consultations [ 27 ]. Due to the shortage of medical personnel and the burden of work, some medical schools also encouraged their students to participate in health care assistance in hospitals during the pandemic [ 28 , 29 ].

The flood of COVID-19 related misinformation has posed an additional threat to and burden on public health. We have an opportunity to address this issue and respond to the general public’s call for guidance from the medical community about COVID-19 by engaging medical students as a main force in the fight against coronavirus related misinformation.

Literature review

The third-person effect in the misinformation context.

Originally proposed by Davison [ 7 ], the third-person effect hypothesizes that people tend to perceive a greater effect of mass media on others than on themselves. Specifically, the TPE consists of two key components: the perceptual and the behavioral [ 16 ]. The perceptual component centers on the perceptual gap where individuals tend to perceive that others are more influenced by media messages than themselves. The behavioral component refers to the behavioral outcomes of the self-other perceptual gap in which people act in accordance with such perceptual asymmetry.

According to Perloff [ 30 ], the TPE is contingent upon situations. For instance, one general finding suggests that when media messages are considered socially undesirable, nonbeneficial, or involving risks, the TPE will get amplified [ 16 ]. Misinformation characterized as inaccurate, misleading, and even false, is regarded as undesirable in nature [ 31 ]. Based on this line of reasoning, we anticipate that people will tend to perceive that others would be more influenced by misinformation than themselves.

Recent studies also provide empirical evidence of the TPE in the context of misinformation [ 32 ]. For instance, an online survey of 511 Chinese respondents conducted by Liu and Huang [ 33 ] revealed that individuals would perceive others to be more vulnerable to the negative influence of COVID-19 digital disinformation. An examination of the TPE within a pre-professional group – the medical students–will allow our study to examine the TPE scholarship in a particular population in the context of tackling misinformation.

Why TPE occurs among medical students: a social identity perspective

Of the works that have provided explanations for the TPE, the well-known ones include self-enhancement [ 34 ], attributional bias [ 35 ], self-categorization theory [ 36 ], and the exposure hypothesis [ 19 ]. In this study, we argue for a social identity perspective as being an important explanation for third-person effects of misinformation among medical students [ 36 , 37 ].

The social identity explanation suggests that people define themselves in terms of their group memberships and seek to maintain a positive self-image through favoring the members of their own groups over members of an outgroup, which is also known as downward comparison [ 38 , 39 ]. In intergroup settings, the tendency to evaluate their ingroups more positively than the outgroups will lead to an ingroup bias [ 40 ]. Such an ingroup bias is typically described as a trigger for the third-person effect as individuals consider themselves and their group members superior and less vulnerable to undesirable media messages than are others and outgroup members [ 20 ].

In the context of our study, medical students highly identified with the medical community tend to maintain a positive social identity through an intergroup comparison that favors the ingroup and derogates the outgroup (i.e., the general public). It is likely that medical students consider themselves belonging to the medical community and thus are more knowledgeable and smarter than the general public in health-related topics, leading them to perceive the general public as more vulnerable to health-related misinformation than themselves. Accordingly, we propose the following hypothesis:

H1: As medical students’ identification with the medical community increases, the TPP concerning digital misinformation will become larger.

What influences the magnitude of TPP

Previous studies have demonstrated that the magnitude of the third-person perception is influenced by a host of factors including efficacy beliefs [ 3 ] and cultural differences in self-construal [ 22 , 23 ]. Self-construal is defined as “a constellation of thoughts, feelings, and actions concerning the relationship of the self to others, and the self as distinct from others” [ 41 ]. Markus and Kitayama (1991) identified two dimensions of self-construal: Independent and interdependent. Generally, collectivists hold an interdependent view of the self that emphasizes harmony, relatedness, and places importance on belonging, whereas individualists tend to have an independent view of the self and thus view themselves as distinct and unique from others [ 42 ]. Accordingly, cultural values such as collectivism-individualism should also play a role in shaping third-person perception due to the adjustment that people make of the self-other social identity distance [ 22 ].

Set in a Chinese context aiming to explore the potential of individual-level approaches to deal with misinformation, this study examines whether collectivism (the prevailing cultural value in China) and self-efficacy (an important determinant of ones’ behavioral intentions) would affect the magnitude of TPP concerning misinformation and how such impact in turn would influence their actions against misinformation.

The impact of self-efficacy on TPP

Bandura [ 43 ] refers to self-efficacy as one’s perceived capability to perform a desired action required to overcome barriers or manage challenging situations. He also suggests understanding self-efficacy as “a differentiated set of self-beliefs linked to distinct realms of functioning” [ 44 ]. That is to say, self-efficacy should be specifically conceptualized and operationalized in accordance with specific contexts, activities, and tasks [ 45 ]. In the context of digital misinformation, this study defines self-efficacy as one’s belief in his/her abilities to identify and verify misinformation within an affordance-bounded social media environment [ 3 ].

Previous studies have found self-efficacy to be a reliable moderator of biased optimism, which indicates that the more efficacious individuals consider themselves, the greater biased optimism will be invoked [ 12 , 23 , 46 ]. Even if self-efficacy deals only with one’s assessment of self in performing a task, it can still create the other-self perceptual gap; individuals who perceive a higher self-efficacy tend to believe that they are more capable of controlling a stressful or challenging situation [ 12 , 14 ]. As such, they are likely to consider themselves less vulnerable to negative events than are others [ 23 ]. That is, individuals with higher levels of self-efficacy tend to underestimate the impact of harmful messages on themselves, thereby widening the other-self perceptual gap.

In the context of fake news, which is closely related to misinformation, scholars have confirmed that fake news efficacy (i.e., a belief in one’s capability to evaluate fake news [ 3 ]) may lead to a larger third-person perception. Based upon previous research evidence, we thus propose the following hypothesis:

H2: As medical students’ digital misinformation self-efficacy increases, the TPP concerning digital misinformation will become larger.

The influence of collectivism on TPP

Originally conceptualized as a societal-level construct [ 47 ], collectivism reflects a culture that highlights the importance of collective goals over individual goals, defines the self in relation to the group, and places great emphasis on conformity, harmony and interdependence [ 48 ]. Some scholars propose to also examine cultural values at the individual level as culture is embedded within every individual and could vary significantly among individuals, further exerting effects on their perceptions, attitudes, and behaviors [ 49 ]. Corresponding to the construct at the macro-cultural level, micro-psychometric collectivism which reflects personality tendencies is characterized by an interdependent view of the self, a strong sense of other-orientation, and a great concern for the public good [ 50 ].

A few prior studies have indicated that collectivism might influence the magnitude of TPP. For instance, Lee and Tamborini [ 23 ] found that collectivism had a significant negative effect on the magnitude of TPP concerning Internet pornography. Such an impact can be understood in terms of biased optimism and social distance. Collectivists tend to view themselves as an integral part of a greater social whole and consider themselves less differentiated from others [ 51 ]. Collectivism thus would mitigate the third-person perception due to a smaller perceived social distance between individuals and other social members and a lower level of comparative optimism [ 22 , 23 ]. Based on this line of reasoning, we thus propose the following hypothesis:

H3: As medical students’ collectivism increases, the TPP concerning digital misinformation will become smaller.

Behavioral consequences of TPE in the misinformation context

The behavioral consequences trigged by TPE have been classified into three categories: restrictive actions refer to support for censorship or regulation of socially undesirable content such as pornography or violence on television [ 52 ]; corrective action is a specific type of behavior where people seek to voice their own opinions and correct the perceived harmful or ambiguous messages [ 53 ]; promotional actions target at media content with desirable influence, such as advocating for public service announcements [ 24 ]. In a word, restriction, correction and promotion are potential behavioral outcomes of TPE concerning messages with varying valence of social desirability [ 16 ].

Restrictive action as an outcome of third-person perceptual bias (i.e., the perceptual component of TPE positing that people tend to perceive media messages to have a greater impact on others than on themselves) has received substantial scholarly attention in past decades; scholars thus suggest that TPE scholarship to go beyond this tradition and move toward the exploration of corrective and promotional behaviors [ 16 , 24 ]. Moreover, individual-level corrective and promotional actions deserve more investigation specifically in the context of countering misinformation, as efforts from networked citizens have been documented as an important supplement beyond institutional regulations (e.g., drafting policy initiatives to counter misinformation) and platform-based measures (e.g., improving platform algorithms for detecting misinformation) [ 8 ].

In this study, corrective action specifically refers to individuals’ reactive behaviors that seek to rectify misinformation; these include such actions as debunking online misinformation by commenting, flagging, or reporting it [ 3 , 54 ]. Promotional action involves advancing correct information online, including in response to misinformation that has already been disseminated to the public [ 55 ].

The impact of TPP on corrective and promotional actions

Either paternalism theory [ 56 ] or the protective motivation theory [ 57 ] can act as an explanatory framework for behavioral outcomes triggered by third-person perception. According to these theories, people act upon TPP as they think themselves to know better and feel obligated to protect those who are more vulnerable to negative media influence [ 58 ]. That is, corrective and promotional actions as behavioral consequences of TPP might be driven by a protective concern for others and a positive sense of themselves.

To date, several empirical studies across contexts have examined the link between TPP and corrective actions. Koo et al. [ 8 ], for instance, found TPP was not only positively related to respondents’ willingness to correct misinformation propagated by others, but also was positively associated with their self-correction. Other studies suggest that TPP motivates individuals to engage in both online and offline corrective political participation [ 59 ], give a thumbs down to a biased story [ 60 ], and implement corrective behaviors concerning “problematic” TV reality shows [ 16 ]. Based on previous research evidence, we thus propose the following hypothesis:

H4: Medical students with higher degrees of TPP will report greater intentions to correct digital misinformation.

Compared to correction, promotional behavior has received less attention in the TPE research. Promotion commonly occurs in a situation where harmful messages have already been disseminated to the public and others appear to have been influenced by these messages, and it serves as a remedial action to amplify messages with positive influence which may in turn mitigate the detrimental effects of harmful messages [ 16 ].

Within this line of studies, however, empirical studies provide mixed findings. Wei and Golan [ 24 ] found a positive association between TPP of desirable political ads and promotional social media activism such as posting or linking the ad on their social media accounts. Sun et al. [ 16 ] found a negative association between TPP regarding clarity and community-connection public service announcements (PSAs) and promotion behaviors such as advocating for airing more PSAs in TV shows.

As promotional action is still underexplored in the TPE research, and existing evidence for the link between TPP and promotion is indeed mixed, we thus propose an exploratory research question:

RQ1: What is the relationship between TPP and medical students’ intentions to promote corrective information?

The impact of self-efficacy and collectivism on actions against misinformation

According to social cognitive theory, people with higher levels of self-efficacy tend to believe they are competent and capable and are more likely to execute specific actions [ 43 ]. Within the context of digital misinformation, individuals might become more willing to engage in misinformation correction if they have enough knowledge and confidence to evaluate information, and possess sufficient skills to verify information through digital tools and services [ 61 ].

Accordingly, we assumed medical students with higher levels of digital misinformation self-efficacy would be likely to become more active in the fight against misinformation.

H5: Medical students with higher levels of digital misinformation self-efficacy will report greater intentions to (a) correct misinformation and (b) promote corrective information on social media.

Social actions of collectivists are strongly guided by prevailing social norms, collective responsibilities, and common interest, goals, and obligations [ 48 ]. Hence, highly collectivistic individuals are more likely to self-sacrifice for group interests and are more oriented toward pro-social behaviors, such as adopting pro-environmental behaviors [ 62 ], sharing knowledge [ 23 ], and providing help for people in need [ 63 ].

Fighting against misinformation is also considered to comprise altruism, especially self-engaged corrective and promotional actions, as such actions are costly to the actor (i.e., taking up time and energy) but could benefit the general public [ 61 ]. Accordingly, we assume collectivism might play a role in prompting people to engage in reactive behaviors against misinformation.

It is also noted that collectivist values are deeply rooted in Chinese society and were especially strongly advocated during the outbreak of COVID-19 with an attempt to motivate prosocial behaviors [ 63 ]. Accordingly, we expected that the more the medical students were oriented toward collectivist values, the more likely they would feel personally obliged and normatively motivated to engage in misinformation correction. However, as empirical evidence was quite limited, we proposed exploratory research questions:

RQ2: Will medical students with higher levels of collectivism report greater intentions to (a) correct misinformation and (b) promote corrective information on social media?

The theoretical model

To integrate both the antecedents and consequences of TPP, we proposed a theoretical model (as shown in Fig. 1 ) to examine how professional identification, self-efficacy and collectivism would influence the magnitude of TPP, and how such impact would in turn influence medical students’ intentions to correct digital misinformation and promote corrective information. Thus, RQ3 was proposed:

RQ3: Will the TPP mediate the impact of self-efficacy and collectivism on medical students’ intentions to (a) correct misinformation, and (b) promote corrective information on social media? Fig. 1 The proposed theoretical model. DMSE = Digital Misinformation Self-efficacy; PIMC = Professional Identification with Medical Community; ICDM = Intention to Correct Digital Misinformation; IPCI = Intention to Promote Corrective Information Full size image

To examine the proposed hypotheses, this study utilized cross-sectional survey data from medical students in Tongji Medical College (TJMC) of China. TJMC is one of the birthplaces of Chinese modern medical education and among the first universities and colleges that offer eight-year curricula on clinical medicine. Further, TJMC is located in Wuhan, the epicenter of the initial COVID-19 outbreaks, thus its students might find the pandemic especially relevant – and threatening – to them.

The survey instrument was pilot tested using a convenience sample of 58 respondents, leading to minor refinements to a few items. Upon approval from the university’s Institutional Research Board (IRB), the formal investigation was launched in TJMC during April 2022. Given the challenges of reaching the whole target population and acquiring an appropriate sampling frame, this study employed purposive and convenience sampling.

We first contacted four school counselors as survey administrators through email with a letter explaining the objective of the study and requesting cooperation. All survey administrators were trained by the principal investigator to help with the data collection in four majors (i.e., basic medicine, clinical medicine, nursing, and public health). Paper-and-pencil questionnaires were distributed to students on regular weekly departmental meetings of each major as students in all grades (including undergraduates, master students, and doctoral students) were required to attend the meeting. The projected time of completion of the survey was approximately 10–15 min. The survey administrators indicated to students that participation was voluntary, their responses would remain confidential and secure, and the data would be used only for academic purposes. Though a total of 1,500 participants took the survey, 17 responses were excluded from the analysis as they failed the attention filters. Ultimately, a total of 1,483 surveys were deemed valid for analysis.

Of the 1,483 respondents, 624 (42.10%) were men and 855 (57.70%) were women, and four did not identify gender. The average age of the sample was 22.00 ( SD  = 2.54, ranging from 17 to 40). Regarding the distribution of respondents’ majors, 387 (26.10%) were in basic medicine, 390 (26.30%) in clinical medicine, 307 (20.70%) in nursing, and 399 (26.90%) in public health. In terms of university class, 1,041 (70.40%) were undergraduates, 291 (19.70%) were working on their master degrees, 146 (9.90%) were doctoral students, and five did not identify their class data.

Measurement of key variables

Perceived effects of digital misinformation on oneself and on others.

Three modified items adapted from previous research [ 33 , 64 ] were employed to measure perceived effects of digital misinformation on oneself. Respondents were asked to indicate to what extent they agreed with the following: (1) I am frequently concerned that the information about COVID-19 I read on social media might be false; (2) Misinformation on social media might misguide my understanding of the coronavirus; (3) Misinformation on social media might influence my decisions regarding COVID-19. The response categories used a 7-point scale, where 1 meant “strongly disagree” and 7 meant “strongly agree.” The measure of perceived effects of digital misinformation on others consisted of four parallel items with the same statement except replacing “I” and “my” with “the general others” and “their”. The three “self” items were averaged to create a measure of “perceived effects on oneself” ( M  = 3.98, SD  = 1.49, α  = 0.87). The three “others” items were also added and averaged to form an index of “perceived effects on others” ( M  = 4.62, SD  = 1.32, α  = 0.87).

The perceived self-other disparity (TPP)

TPP was derived by subtracting perceived effects on oneself from perceived effects on others.

Professional identification with medical community

Professional identification was measured using a three item, 7-point Likert-type scale (1 =  strongly disagree , 7 =  strongly agree ) adapted from previous studies [ 65 , 66 ] by asking respondents to indicate to what extent they agreed with the following statements: (1) I would be proud to be a medical staff member in the future; (2) I am committed to my major; and (3) I will be in an occupation that matches my current major. The three items were thus averaged to create a composite measure of professional identification ( M  = 5.34, SD  = 1.37, α  = 0.88).

Digital misinformation self-efficacy

Modified from previous studies [ 3 ], self-efficacy was measured with three items. Respondents were asked to indicate on a 7-point Linkert scale from 1 (strongly disagree) to 7 (strongly agree) their agreement with the following: (1) I think I can identify misinformation relating to COVID-19 on social media by myself; (2) I know how to verify misinformation regarding COVID-19 by using digital tools such as Tencent Jiaozhen Footnote 1 and Piyao.org.cn Footnote 2 ; (3) I am confident in my ability to identify digital misinformation relating to COVID-19. A composite measure of self-efficacy was constructed by averaging the three items ( M  = 4.38, SD  = 1.14, α  = 0.77).

• Collectivism

Collectivism was measured using four items adapted from previous research [ 67 ], in which respondents were asked to indicate their agreement with the following statements on a 7-point scale, from 1 (strongly disagree) to 7 (strongly agree): (1) Individuals should sacrifice self-interest for the group; (2) Group welfare is more important than individual rewards; (3) Group success is more important than individual success; and (4) Group loyalty should be encouraged even if individual goals suffer. Therefore, the average of the four items was used to create a composite index of collectivism ( M  = 4.47, SD  = 1.30, α  = 0.89).

Intention to correct digital misinformation

We used three items adapted from past research [ 68 ] to measure respondents’ intention to correct misinformation on social media. All items were scored on a 7-point scale from 1 (very unlikely) to 7 (very likely): (1) I will post a comment saying that the information is wrong; (2) I will message the person who posts the misinformation to tell him/her the post is wrong; (3) I will track the progress of social media platforms in dealing with the wrong post (i.e., whether it’s deleted or corrected). A composite measure of “intention to correct digital misinformation” was constructed by adding the three items and dividing by three ( M  = 3.39, SD  = 1.43, α  = 0.81).

Intention to promote corrective information

On a 7-point scale ranging from 1 (very unlikely) to 7 (very likely), respondents were asked to indicate their intentions to (1) Retweet the corrective information about coronavirus on my social media account; (2) Share the corrective information about coronavirus with others through Social Networking Services. The two items were averaged to create a composite measure of “intention to promote corrective information” ( M  = 4.60, SD  = 1.68, r  = 0.77).

Control variables

We included gender, age, class (1 = undergraduate degree; 2 = master degree; 3 = doctoral degree), and clinical internship (0 = none; 1 = less than 0.5 year; 2 = 0.5 to 1.5 years; 3 = 1.5 to 3 years; 4 = more than 3 years) as control variables in the analyses. Additionally, coronavirus-related information exposure (i.e., how frequently they were exposed to information about COVID-19 on Weibo, WeChat, and QQ) and misinformation exposure on social media (i.e., how frequently they were exposed to misinformation about COVID-19 on Weibo, WeChat, and QQ) were also assessed as control variables because previous studies [ 69 , 70 ] had found them relevant to misinformation-related behaviors. Descriptive statistics and bivariate correlations between main variables were shown in Table 1 .

Statistical analysis

We ran confirmatory factor analysis (CFA) in Mplus (version 7.4, Muthén & Muthén, 1998) to ensure the construct validity of the scales. To examine the associations between variables and tested our hypotheses, we performed structural equation modeling (SEM). Mplus was chosen over other SEM statistical package mainly because the current data set included some missing data, and the Mplus has its strength in handling missing data using full-information maximum likelihood imputation, which enabled us to include all available data [ 71 , 72 ]. Meanwhile, Mplus also shows great flexibility in modelling when simultaneously handling continuous, categorical, observed, and latent variables in a variety of models. Further, Mplus provides a variety of useful information in a concise manner [ 73 ].

Table 2 shows the model fit information for the measurement and structural models. Five latent variables were specified in the measurement model. To test the measurement model, we examined the values of Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) (Table 1 ). Cronbach’s alpha values ranged from 0.77 to 0.89. The CRs, which ranged from 0.78 to 0.91, exceeded the level of 0.70 recommended by Fornell (1982) and thus confirmed the internal consistency. The AVE estimates, which ranged from 0.54 to 0.78, exceeded the 0.50 lower limit recommended by Fornell and Larcker (1981), and thus supported convergent validity. All the square roots of AVE were greater than the off-diagonal correlations in the corresponding rows and columns [ 74 ]. Therefore, discriminant validity was assured. In a word, our measurement model showed sufficient convergence and discriminant validity.

Five model fit indices–the relative chi-square ratio (χ 2 / df ), the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root-mean-square residual (SRMR) were used to assess the model. Specifically, the normed chi-square between 1 and 5 is acceptable [ 75 ]. TLI and CFI over 0.95 are considered acceptable, SRMR value less than 0.08 and RMSEA value less than 0.06 indicate good fit [ 76 ]. Based on these criteria, the model was found to have an acceptable fit to the data.

Figure 2 presents the results of our hypothesized model. H1 was rejected as professional identification failed to predict TPP ( β  = 0.06, p  > 0.05). Self-efficacy was positively associated with TPP ( β  = 0.14, p  < 0.001) while collectivism was negatively related to TPP ( β  = -0.10, p  < 0.01), lending support to H2 and H3.

Note. N  = 1,483. The coefficients of relationships between latent variables are standardized beta coefficients. Significant paths are indicated by solid line; non-significant paths are indicated by dotted lines. * p  < .05, ** p  < .01; *** p  < .001. DMSE = Digital Misinformation Self-efficacy; PIMC = Professional Identification with Medical Community; ICDM = Intention to Correct Digital Misinformation; IPCI = Intention to Promote Corrective Information

H4 posited that medical students with higher degrees of TPP would report greater intentions to correct digital misinformation. However, we found a negative association between TPP and intentions to correct misinformation ( β  = -0.12, p  < 0.001). H4 was thus rejected. Regarding RQ1, results revealed that TPP was negatively associated with intentions to promote corrective information ( β  = -0.08, p  < 0.05).

Further, our results supported H5 as we found that self-efficacy had a significant positive relationship with corrective intentions ( β  = 0.18, p  < 0.001) and promotional intentions ( β  = 0.32, p  < 0.001). Collectivism was also positively associated with intentions to correct misinformation ( β  = 0.14, p  < 0.001) and promote corrective information ( β  = 0.20, p  < 0.001), which answered RQ2.

Regarding RQ3 (see Table 3 ), TPP significantly mediated the relationship between self-efficacy and intentions to correct misinformation ( β  = -0.016), as well as the relationship between self-efficacy and intentions to promote corrective information ( β  = -0.011). However, TPP failed to mediate either the association between collectivism and corrective intentions ( β  = 0.011, ns ) or the association between collectivism and promotional intentions ( β  = 0.007, ns ).

Recent research has highlighted the role of health professionals and scientists in the fight against misinformation as they are considered knowledgeable, ethical, and reliable [ 5 , 77 ]. This study moved a step further by exploring the great potential of pre-professional medical students to tackle digital misinformation. Drawing on TPE theory, we investigated how medical students perceived the impact of digital misinformation, the influence of professional identification, self-efficacy and collectivism on these perceptions, and how these perceptions would in turn affect their actions against digital misinformation.

In line with prior studies [ 3 , 63 ], this research revealed that self-efficacy and collectivism played a significant role in influencing the magnitude of third-person perception, while professional identification had no significant impact on TPP. As shown in Table 1 , professional identification was positively associated with perceived effects of misinformation on oneself ( r  = 0.14, p  < 0.001) and on others ( r  = 0.20, p  < 0.001) simultaneously, which might result in a diminished TPP. What explains a shared or joint influence of professional identification on self and others? A potential explanation is that even medical staff had poor knowledge about the novel coronavirus during the initial outbreak [ 78 ]. Accordingly, identification with the medical community was insufficient to create an optimistic bias concerning identifying misinformation about COVID-19.

Our findings indicated that TPP was negatively associated with medical students’ intentions to correct misinformation and promote corrective information, which contradicted our hypotheses but was consistent with some previous TPP research conducted in the context of perceived risk [ 10 , 79 , 80 , 81 ]. For instance, Stavrositu and Kim (2014) found that increased TPP regarding cancer risk was negatively associated with behavioral intentions to engage in further cancer information search/exchange, as well as to adopt preventive lifestyle changes. Similarly, Wei et al. (2008) found concerning avian flu news that TPP negatively predicted the likelihood of engaging in actions such as seeking relevant information and getting vaccinated. In contrast, the perceived effects of avian flu news on oneself emerged as a positive predictor of intentions to take protective behavior.

Our study shows a similar pattern as perceived effects of misinformation on oneself were positively associated with intentions to correct misinformation ( r  = 0.06, p  < 0.05) and promote corrective information ( r  = 0.10, p  < 0.001, See Table 1 ). While the reasons for the behavioral patterns are rather elusive, such findings are indicative of human nature. When people perceive misinformation-related risk to be highly personally relevant, they do not take chances. However, when they perceive others to be more vulnerable than themselves, a set of sociopsychological dynamics such as self-defense mechanism, positive illusion, optimistic bias, and social comparison provide a restraint on people’s intention to engage in corrective and promotional actions against misinformation [ 81 ].

In addition to the indirect effects via TPP, our study also revealed that self-efficacy and collectivism serve as direct and powerful drivers of corrective and promotive actions. Consistent with previous literature [ 61 , 68 ], individuals will be more willing to engage in social corrections of misinformation if they possess enough knowledge, skills, abilities, and resources to identify misinformation, as correcting misinformation is difficult and their effort would not necessarily yield positive outcomes. Collectivists are also more likely to engage in misinformation correction as they are concerned for the public good and social benefits, aiming to protect vulnerable people from being misguided by misinformation [ 82 ].

This study offers some theoretical advancements. First, our study extends the TPE theory by moving beyond the examination of restrictive actions and toward the exploration of corrective and promotional actions in the context of misinformation. This exploratory investigation suggests that self-other asymmetry biased perception concerning misinformation did influence individuals’ actions against misinformation, but in an unexpected direction. The results also suggest that using TPP alone to predict behavioral outcomes was deficient as it only “focuses on differences between ‘self’ and ‘other’ while ignoring situations in which the ‘self’ and ‘other’ are jointly influenced” [ 83 ]. Future research, therefore, could provide a more sophisticated understanding of third-person effects on behavior by comparing the difference of perceived effects on oneself, perceived effects on others, and the third-person perception in the pattern and strength of the effects on behavioral outcomes.

Moreover, institutionalized corrective solutions such as government and platform regulation are non-exhaustive [ 84 , 85 ]; it thus becomes critical to tap the great potential of the crowd to engage in the fight against misinformation [ 8 ] while so far, research on the motivations underlying users’ active countering of misinformation has been scarce. The current paper helps bridge this gap by exploring the role of self-efficacy and collectivism in predicting medical students’ intentions to correct misinformation and promote corrective information. We found a parallel impact of the self-ability-related factor and the collective-responsibility-related factor on intentions to correct misinformation and promote corrective information. That is, in a collectivist society like China, cultivating a sense of collective responsibility and obligation in tackling misinformation (i.e., a persuasive story told with an emphasis on collective interests of social corrections of misinformation), in parallel with systematic medical education and digital literacy training (particularly, handling various fact-checking tools, acquiring Internet skills for information seeking and verification) would be effective methods to encourage medical students to engage in active countering behaviors against misinformation. Moreover, such an effective means of encouraging social corrections of misinformation might also be applied to the general public.

In practical terms, this study lends new perspectives to the current efforts in dealing with digital misinformation by involving pre-professionals (in this case, medical students) into the fight against misinformation. As digital natives, medical students usually spend more time online, have developed sophisticated digital competencies and are equipped with basic medical knowledge, thus possessing great potential in tackling digital misinformation. This study further sheds light on how to motivate medical students to become active in thwarting digital misinformation, which can help guide strategies to enlist pre-professionals to reduce the spread and threat of misinformation. For example, collectivism education in parallel with digital literacy training would help increase medical students’ sense of responsibility for and confidence in tackling misinformation, thus encouraging them to engage in active countering behaviors.

This study also has its limitations. First, the cross-sectional survey study did not allow us to justify causal claims. Granted, the proposed direction of causality in this study is in line with extant theorizing, but there is still a possibility of reverse causal relationships. To establish causality, experimental research or longitudinal studies would be more appropriate. Our second limitation lies in the generalizability of our findings. With the focus set on medical students in Chinese society, one should be cautious in generalizing the findings to other populations and cultures. For example, the effects of collectivism on actions against misinformation might differ in Eastern and Western cultures. Further studies would benefit from replication in diverse contexts and with diverse populations to increase the overall generalizability of our findings.

Drawing on TPE theory, our study revealed that TPP failed to motivate medical students to correct misinformation and promote corrective information. However, self-efficacy and collectivism were found to serve as direct and powerful drivers of corrective and promotive actions. Accordingly, in a collectivist society such as China’s, cultivating a sense of collective responsibility in tackling misinformation, in parallel with efficient personal efficacy interventions, would be effective methods to encourage medical students, even the general public, to actively engage in countering behaviors against misinformation.

Availability of data and materials

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

Tencent Jiaozhen Fact-Checking Platform which comprises the Tencent information verification tool allow users to check information authenticity through keyword searching. The tool is updated on a daily basis and adopts a human-machine collaboration approach to discovering, verifying, and refuting rumors and false information. For refuting rumors, Tencent Jiaozhen publishes verified content on the homepage of Tencent's rumor-refuting platform, and uses algorithms to accurately push this content to users exposed to the relevant rumors through the WeChat dispelling assistant.

Piyao.org.cn is hosted by the Internet Illegal Information Reporting Center under the Office of the Central Cyberspace Affairs Commission and operated by Xinhuanet.com. The platform is a website that collects statements from Twitter-like services, news portals and China's biggest search engine, Baidu, to refute online rumors and expose the scams of phishing websites. It has integrated over 40 local rumor-refuting platforms and uses artificial intelligence to identify rumors.

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Acknowledgements

We thank all participants and staff working for the project.

This work was supported by Humanities and Social Sciences Youth Foundation of the Ministry of Education of China (Grant No. 21YJC860012).

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Li, Z., Yan, J. Does a perceptual gap lead to actions against digital misinformation? A third-person effect study among medical students. BMC Public Health 24 , 1291 (2024). https://doi.org/10.1186/s12889-024-18763-9

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Resident worklife and wellness through the late phase of the pandemic: a mixed methods national survey study

• Mark Linzer 1 ,
• Sanjoyita Mallick 2 ,
• Purva Shah 3 ,
• Anne Becker 2 ,
• Nancy Nankivil 3 ,
• Sara Poplau 1 ,
• Shivani K. Patel 3 ,
• Caitlin Nosal 3 ,
• Christine A. Sinsky 3 ,
• Elizabeth Goelz 1 ,
• Martin Stillman 1 ,
• Michaella Alexandrou 4 ,
• Erin E. Sullivan 5 &
• Roger Brown 6  

BMC Medical Education volume  24 , Article number:  484 ( 2024 ) Cite this article

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System contributors to resident burnout and well-being have been under-studied. We sought to determine factors associated with resident burnout and identify at risk groups.

We performed a US national survey between July 15 2022 and April 21, 2023 of residents in 36 specialties in 14 institutions, using the validated Mini ReZ survey with three 5 item subscales: 1) supportive workplace, 2) work pace/electronic medical record (EMR) stress, and 3) residency-specific factors (sleep, peer support, recognition by program, interruptions and staff relationships). Multilevel regressions and thematic analysis of 497 comments determined factors related to burnout.

Of 1118 respondents (approximate median response rate 32%), 48% were female, 57% White, 21% Asian, 6% LatinX and 4% Black, with 25% PGY 1 s, 25% PGY 2 s, and 22% PGY 3 s. Programs included internal medicine (15.1%) and family medicine (11.3%) among 36 specialties. Burnout (found in 42%) was higher in females (51% vs 30% in males, p  = 0.001) and PGY 2’s (48% vs 35% in PGY-1 s, p  = 0.029). Challenges included chaotic environments (41%) and sleep impairment (32%); favorable aspects included teamwork (94%), peer support (93%), staff support (87%) and program recognition (68%). Worklife subscales were consistently lower in females while PGY-2’s reported the least supportive work environments. Worklife challenges relating to burnout included sleep impairment (adjusted Odds Ratio (aOR) 2.82 (95% CIs 1.94, 4.19), absolute risk difference (ARD) in burnout 15.9%), poor work control (aOR 2.25 (1.42, 3.58), ARD 12.2%) and chaos (aOR 1.73 (1.22, 2.47), ARD 7.9%); program recognition was related to lower burnout (aOR 0.520 (0.356, 0.760), ARD 9.3%). These variables explained 55% of burnout variance. Qualitative data confirmed sleep impairment, lack of schedule control, excess EMR and patient volume as stressors.

Conclusions

These data provide a nomenclature and systematic method for addressing well-being during residency. Work conditions for females and PGY 2’s may merit attention first.

Peer Review reports

Introduction

The pandemic produced upheavals in worklife for practicing clinicians and staff. While national studies have assessed worklife in practicing physicians [ 1 , 2 , 3 , 4 ] and staff [ 5 ], fewer have addressed resident worklife [ 6 ]. Much of the literature is from the 2000’s and 2010’s [ 7 , 8 , 9 ], and most studies employ data from small numbers of residents and programs. Burnout prevalence rates vary considerably, from 35 to 76% [ 7 , 8 , 9 ]. Yet little is available to determine how residents traversed the pandemic, and how to prepare for future surges in stress.

We reviewed recent data (July 2022 to April 2023) from residency programs surveyed by the American Medical Association (AMA) using the Mini ReZ, a validated measure [ 10 ] derived from the Mini Z [ 11 ] assessing burnout with a single item validated against the Maslach Burnout Inventory (MBI) emotional exhaustion (EE) scale [ 12 ], and several items addressing known components of burnout [ 13 , 14 ], as well as 5 items derived from Trockel [ 15 ] defining work conditions related to resident burnout (interruptions, sleep impairment, support staff relationships, recognition by program and peer support). Study objectives were to determine 1) burnout prevalence, 2) program characteristics associated with favorable burnout rates, 3) gender differences in resident burnout (found previously in faculty and practicing clinicians), and 4) differences in work conditions by Post Graduate Year (PGY), anticipating that PGY 1 year would be most stressful. We used qualitative analysis in a “complementarity” manner to enhance findings from quantitative scales, focusing on remediable correlates of burnout.

In 2017, the AMA began surveying residencies using the Mini Z for residents (Mini ReZ). For this paper we focus on 14 institutions and 1118 residents surveyed from July 15, 2022 through April 21, 2023. Residents trained in varied specialties (see Supplemental Table  1A ), with the most in internal medicine, family medicine and emergency medicine. Response rates, determined by institution, allowed calculation of an overall median rate.

Study design

Residents were surveyed anonymously, typically once per year. Organizations performed their own surveys, and results were aggregated in the affiliated Data Lab.

The Mini ReZ (Supplemental Fig.  1A ) uses the core Mini Z 10 item structure, assessing outcomes (satisfaction, stress and burnout), and work conditions (work control, chaotic environments, teamwork, values alignment and electronic medical record (EMR) experience) using 5-point scales [ 11 ]. Five items were added to reflect findings from Trockel [ 16 ] of domains critical to resident wellness (interruptions, sleep impairment, support staff relationships, program recognition and peer support). Questions were aligned from low to high (high score = positive attribute). Items were dichotomized with the top 2 or 3 choices scored as, e.g., “good control”, “no chaos”, or “efficient teamwork”. Details on subscales, scoring [ 17 ] and validation [ 18 , 19 ] are in the Technical Appendix . In brief, a summary score of 75 (5 × 15, range 15–75, > 80% = a “joyous workplace”) is created, consisting of three 5 item subscales: 1) supportive work environment (range 5–25, target = 20 or higher), 2) work pace/EMR stress (range 5–25, target of 20 or higher) and 3) resident specific factors (sleep, interruptions, peer and staff support, and program recognition, range 5–25, target 20 or higher).

Burnout by predictor variables (satisfaction, chaos (work atmosphere), values alignment, recognition by program, lack of work control, stress, documentation time pressure and sleep impairment) in 1118 residents in national Mini ReZ survey July 2022 to April 2023. “High” = variable present (e.g. high satisfaction, top two scores), “low” means variable had lower scores

Quantitative analysis

Bivariate comparisons were performed using Chi square, t tests and Fisher’s exact test, correcting for multiple comparisons with the FDR (False Discovery Rate). Higher scores were collapsed into binary variables, noting “presence” of a variable (e.g. values alignment) vs absence. Multivariate regressions determined remediable correlates of burnout. A p value of < 0.05 was considered statistically significant. Absolute differences of 5–10% in burnout rates or in prevalence of work conditions were considered clinically meaningful, correlating with an Effect Size (ES) of 0.1 to 0.2. Forest plots assessed standardized mean differences, with ESs representing important differences between genders and PGY years (1, 2, 3 or 4/5/Fellow).

Qualitative analysis

[ 20 ] Thematic analysis assessed additional factors related to burnout. Responses to the open-ended question, “Tell us more about your current stressors and ideas you have for minimizing them,” were analyzed using an inductive, thematic approach. First, comments were reviewed to identify emerging and recurrent themes. Comments were then thematically indexed and coded using NVivo 12. Co-authors reviewed results and reached consensus on how qualitative data contextualized quantitative findings.

Qualitative data which enhanced interpretation of quantitative data were merged with quantitative findings in line with theoretical constructs of the Job-Demands Resources (JD-R) [ 21 ] and Demand-Control Models of job stress [ 22 ], as well as healthcare-related application of these models in the MEMO study (Minimizing Error Maximizing Outcome) [ 13 ] and Healthy Work Place trial [ 23 ], to create a conceptual model of worklife and well-being in residents.

The Hennepin Healthcare Institutional Review Board (IRB) determined this work was exempt from human subjects research requirements.

Demographics

There were 1118 respondents in 14 institutions (with 36 program types listed in Supplemental Table  1A ). Median response rate was approximately 32% (25th percentile 19%, 75th percentile 94%, interquartile range 75%). Respondents were located in the Midwest ( N  = 179, 16.0%), Northeast ( N  = 530, 47.4%), Southern ( N  = 244, 21.8%) and Western ( N  = 165, 14.8%) US regions. Of respondents (Table  1 ), 507 (46%) were male, 529 (48%) female, and 66 (6%) preferred not to identify gender (PNTI-g); 598 (57%) identified as White, 220 (21%) Asian, 61 (6%) Latinx, 47 (4%) Black, and 119 (11%) preferred not to identify race or ethnicity (PNTI-r). For year of training, 25% were PGY 1’s, 25% PGY 2’s, 22% PGY 3’s, with the remainder PGY 4’s, 5’s and Fellows.

Summary score and subscales (Table  1 )

The summary score and 3 subscale scores were all less than target (80% of possible top score). Three were > 65% of total possible score, while one scale (“work pace/EMR stress”) was moderately lower at 58% of possible.

For individual worklife item prevalence , program satisfaction was high in 83% of residents (Tables 2 and 3 ). Burnout was present in 42%, higher in females (51%) and highest in those preferring not to identify gender (56%) or race (57%). Values alignment with leaders, a correlate of lower burnout [ 13 ] in practicing physicians, was high in 78% of residents, while teamwork, related to lower burnout in clinical practice [ 24 ], was rated highly by 94%. Lack of work control, a factor associated with burnout during the pandemic [ 5 ] and in prior years [ 25 ] in clinicians, was poor or marginal in 22%, while high stress, an antecedent of burnout, was noted by 44%. High home EMR use was noted by 34%, and chaotic environments, another burnout correlate [ 26 ] in practicing physicians, were described by 41% of residents.

Of resident-specific domains, sleep impairment, a burnout correlate [ 16 ], was noted by 32%, positive relationships with support staff were described by 87%, peer support (typically felt to refer to support by resident peers) was noted by 93%, while recognition by program was noted by 68%. Burnout—work environment graphs (Fig.  1 ) show lower burnout with high satisfaction, values alignment and recognition by program, and higher burnout in the presence of stress, chaos (work atmosphere), lack of work control, documentation (EMR) pressures and sleep impairment (all p’s < 0.05).

• Gender differences

Burnout was higher among females vs males (51% vs 30%, p  = 0.001). Other variables were consistently poorer in females, including poor work control (26% poor or marginal control in females vs 16% in males, p  = 0.001), high stress (52% highly stressed in females vs 33% of males, p  = 0.001), high home EMR use (in 37% of females vs 29% of males, p  = 0.019), chaotic workplaces (in 45% of females vs 35% in males, p  = 0.001), and sleep impairment (in 35% of females vs 28% of males, p  = 0.019). Summary scores (49.4 (out of 75) in females vs 53.6 in males, absolute difference 4.25, adjusted p  = 0.001), and all 3 subscales (supportive environment, work pace/EMR stress, and resident-specific items) were significantly lower in females (adjusted p values = 0.001).

Program year

Differences were also seen by program year. High satisfaction was most often seen (88% of the time) in PGY 1’s vs 82% or lower in PGY 2’s and 3’s; burnout was less often seen in PGY 1’s at 35% of the time (vs 48% in PGY 2’s ( p  = 0.029) and 47% in PGY 3’s, p  = 0.083). Efficient teamwork was endorsed by 98% of PGY 1’s, vs 91% in PGY 2’s ( p  = 0.024) and 94% in PGY 3’s ( p  = 0.163). The most frequent endorsement of excessive home EMR time was by PGY 2’s at 40%. Sleep impairment was noted equally as often by PGY 2’s as PGY 1’s (38%). Recognition by one’s program was noted least often by PGY 2’s (64%), although the difference with PGY 1’s (70% recognized) was not statistically significant. The supportive work environment subscale (18.3 vs 19.4) was lower in PGY 2’s vs PGY 1’s (adjusted p  = 0.011).

The Forest plot in Supplemental Fig.  2A assesses subscale scores by gender and year, including PGY 1’s, 2’s, 3’s and 4’s/5’s/Fellows as a final category. Fellows had favorable findings, and SMDs (standardized mean differences, or Effect Sizes) showed prominent differences for males vs females (small to moderate ESs favoring males) for all 3 subscales. Greater challenges were seen for those not identifying gender.

Regression analyses assessing potential components of burnout

In multivariate regressions controlling for gender and year of training (Table  4 ), favorable worklife aspects included program satisfaction (adjusted Odds Ratio (aOR) in association with burnout 0.415, p  = 0.002) and recognition by program (aOR 0.606, p  = 0.012), while challenging factors included stress (aOR 4.47 for greater burnout, p  < 0.001), sleep impairment (aOR 2.58, p  < 0.001), lack of work control (aOR 2.04, p  = 0.003) and chaos (aOR 1.69, p  = 0.004). The full regression model (Table  4 and Supplemental Table  2A ) explained 55% of variance in burnout.

Weekly time spent on different activities

In describing time spent, 18% had 6 h/week or more of home EMR time. In the average 63.6 h work week, there were 24.5 h direct patient care, 21 h indirect care, 7.2 h administrative work, 5.3 h teaching, and 3.2 h research. There was considerable variability in EMR time, with 305 residents (53.4% of 571 responding) spending 20 h per week or less on indirect care activities, 137 (24.0%) spending 20–30 h per week, 71 (12.4%) spending 30–40 h per week, and 58 (10.2%) spending > 40 h per week on indirect care. Thus 47% spent more than 20 h per week on the EMR, while 53% spent less than 20 h per week.

Qualitative findings

There were 497 comments for analysis, once blank and N/A responses were removed. Major themes related to 1) individual-level activities, 2) residency-specific issues or 3) system-level challenges.

Individual-level activities encompassed self-care practices, including adequate sleep, healthy meals, exercise, and time spent with family and friends. Respondents reported difficulty finding balance between work and home life, with some preferring to focus on wellness away from work. A female PGY 2 expressed that she had ‘no time to make her doctor’s appointments, much less find time to exercise’.

Themes related to residency programs included requests for structured curricula, a desire for more program director/attending support, need for control over one’s schedule, and acknowledging the difficult learning curve generated by yearly transitions. A male PGY 1 noted, “Major stresses include being new on my teams, learning the systems, and better understanding my role.”

System challenges included excessive workload, insufficient resources and staff, lack of leader support, and disproportionate time spent on documentation. One female PGY 2 related “I have been working too many unsustainable hours… I come home and I have even more documentation… none of that [documentation] time… is even counted in my working hours. I am completely drained, feeling under-appreciated and very burned out.” The experience of working within broken systems was expressed by one female PGY 3: “…these problems are not unique to (our) residency…: residents in the US are learning and training in a broken healthcare system.”

These findings, with qualitative data enhancing the list of contributing variables, allowed construction of a conceptual model (Supplemental Fig.  3 A) illustrating work conditions associated with residents’ burnout. While most variables were tested in this study (in bold in the Figure), some seen in prior studies await future investigation.

Our national study in 36 different types of residency programs with current data in 1118 residents provides the substrate to answer a recently posed question concerning resident wellness after the pandemic: “How does healing occur?” [ 27 ] We found burnout was prevalent (42%), though somewhat less frequent than pre-pandemic (45% [ 7 ]) and less frequent than in currently practicing physicians (48% [ 5 ], and > 50% [ 28 , 29 ]). Effective teamwork, peer support and staff support were high (endorsed by 87–94% of residents), and may have protected against higher burnout. Values alignment with leadership was strongly associated with lower burnout. Meanwhile, burnout was accompanied by lack of work control, sleep impairment, and chaotic environments. While recognition by programs related to lower burnout, it was only present in 2/3 of residents; this may represent an opportunity for improvement if confirmed in further investigations. Work conditions in females were less favorable in most areas, with all work environment subscales substantively lower (poorer) for females. PGY 1’s had the most favorable scores among PGY 1’s, 2’s and 3’s, and PGY 2’s had poorer scores in several areas with less supportive work environments. Finally, EMR time varied considerably, and was a concern in open-ended comments. Due to convenience sampling and allowing for multiple comparisons to identify potential remediable worklife factors, these findings should be viewed as exploratory; yet they also paint a picture of worklife in residency with specific areas for improvement and some areas of success (peer support, values alignment and teamwork) to maintain and build upon.

Our data close gaps in the literature by 1) presenting national findings for worklife factors related to burnout in a large and diverse sample of residents and residencies, 2) describing the prevalence of key aspects of favorable work cultures and community building, including peer support, teamwork and staff relationships, 3) highlighting the need to learn more about the details of the potential impact of sleep impairment , 4) noting recognition by program as a potential means to reduce burnout, 5) demonstrating persistent and seemingly worsening findings of gender differences in burnout and 6) describing contributors to less supportive environments among PGY 2’s. The literature has shown indicators of burnout within medical residents [ 7 , 8 , 9 , 30 ], and a wide range of burnout prevalence (from 25–75%). Dyrbye’s national studies published in 2018 [ 7 ] demonstrate higher rates of burnout in female and PGY-2 residents, but little information on differences in work conditions. Rodrigues, in 2018 [ 9 ] demonstrated overall burnout rates of 35%. While Nene’s recent blogpost [ 31 ] resonates with Ishak’s list of proposed system changes [ 8 ], including workload reduction, mentoring, and work family balance, and individual interventions such as stress management and meditation, the impact of these strategies remains to be tested.

There are reaffirming findings in our data of what has occurred to build a community around residents , including a high prevalence of peer support, clinical staff support and teamwork. In some subgroups, these were strikingly high (e.g. good to excellent teamwork endorsed by 98% of PGY 1’s). With strong literature evidence for these workplace attributes [ 24 ], the worklife aspects presented here comprise a foundation for measurement and monitoring to allow program directors to determine effectiveness of their support systems.

Regression analyses determined remediable factors that are related to burnout , including sleep impairment, lack of work control and fast paced, chaotic environments. While burnout has diminished with duty hour restrictions [ 32 , 33 ], it has not been eliminated; sleep impairment was described by a third of residents and, with confirmation in future studies and more details of aspects of sleep impairment that are most prevalent, may represent an opportunity for improvement, with customized schedules (e.g., with jeopardy call back-up [ 14 ]) to address sleep challenges in real time. Work control was a major factor for burnout in the early pandemic [ 1 ], and work overload currently contributes to burnout across the healthcare workforce [ 34 ]; customizing workloads to individuals’ work capacity could be tested as a means to reduce burnout and distress. Finally, chaos (fast-paced, hectic workplaces) has been a challenge for physicians [ 26 ], yet few programs have developed metrics to monitor and adjust workplaces (e.g. using human-centered design) for more calm and reasonable workplaces. We propose these factors (sleep, work control and chaos) as part of a program’s Key Performance Indicator (KPI) worklife dashboard.

While gender differences have long been known, with higher burnout rates among female physicians in practice and academia [ 35 , 36 ], their prevalence in residents has recently been noted [ 7 , 37 ], though described in mainly small, localized studies, or with only modest differences (7.6% risk difference in 2018) [ 7 ]. Our findings suggest an absolute burnout increase of over 20% in females, with most worklife items showing poorer scores among female residents, including control, chaos, home EMR use, program recognition and sleep impairment. Other potential contributors include parental responsibilities, harassment and discrimination [ 38 ], gendered expectations for listening [ 39 ], excess “invisible work” in female physicians [ 40 ] and low autonomy [ 41 ]. Strategies to reduce gender differences [ 39 ] include improving understanding of lived experiences, creating interventions to value invisible work, addressing EMR inequities [ 42 , 43 ], and providing greater control of workload to mesh with off-duty responsibilities. With monitoring and transparency, gender inequities can, we believe, be reduced and, eventually, eliminated.

We found an excess of burnout in those preferring not to identify (PNTI) gender or race , with burnout rates of approximately 56% vs 42% in others. Prior studies demonstrated high burnout among LGBTQ students compared with heterosexual students [ 44 ]. Thus, surveys may be missing input from high-stress gender and racial groups; additional efforts are warranted to determine how to best reach out to these groups of trainees.

An unanticipated finding was the low rate of burnout among PGY 1’s and challenging work conditions of PGY 2’s . Norvell [ 37 ] suggests a program for residents transitioning from PGY-1 to PGY 2; others propose a PGY 2 curriculum. In internal medicine programs, the stress of fellowship applications is often highest during the PGY 2 year. Worklife factors meriting attention include home EMR use, sleep impairment, teamwork and program recognition. Supportive work environment subscales were lowest among PGY 2’s (small to moderate Effect Size vs PGY 1’s, p  < 0.001). Thus, attention to the PGY 2 year seems warranted.

Qualitative findings demonstrated 3 themes: individual-level factors, residency-specific aspects, and system-level problems. Self-care needs included available time to rest/sleep, exercise, connect with friends, balance work with family, and take care of one’s own health (e.g. doctor’s appointments). Meditation, mentioned by only a few respondents, was related to low burnout in one randomized trial [ 45 ] while exercise led to burnout reductions in a pre-post trial [ 46 ]. In the current study, residents proposed areas for change, including better curricula, control of schedule, mitigation of long hours, support with year-to-year transitions, workload adjustment, program leader support, and more explicitly being valued.

These factors, along with pandemic-specific frustrations such as lack of support staff, and the quantitative findings noted above, comprise a conceptual model explaining resident burnout (Supplemental Fig.  3A ). The 55% of variance in burnout explained by quantitatively measured variables in this model is among the highest in reported physician burnout models.

For interventions , Vijay and Yancy [ 27 ] propose “changing the vernacular” of what is a good doctor during training from one always present, to one with good team participation, work-life balance and valuing life moments inside and outside of work. They describe residents’ appreciation of the Hopkins Bayview Aliki Service, with fewer patients per resident, attention to social determinants of health, and deeper connections with patients and community. They note a need for time to recover from traumatic events, highlighting recovery programs from recent traumas. Our methods provide a useful means of supporting these suggestions, with a focus on measurement and benchmarking of worklife factors, alleviating gender differences, improving PGY 2 work conditions, addressing EMR excess (e.g. with scribes [ 47 ]), assessing workload and upgrading parental leave policies [ 31 , 48 ]. Recognizing residents’ efforts, straightforward and inexpensive, could quickly address satisfaction and sense of community in women and PGY 2s.

Our work has several limitations and strengths . While ours is a convenience sample, it is a national sample including measurement of worklife among residents and fellows in dozens of program types for which there are few precedents. The 32% response rate, though less than optimal, exceeds the standard 7–20% response rates of national physician surveys [ 29 , 49 ]; we also have little if any information which could allow us to estimate the degree of non-response bias, and some organizational response rates were estimated or inaccurate. While the burnout item is validated against mainly the emotional exhaustion subscale in the Maslach instrument, other Mini Z items correlate with exhaustion and depersonalization [ 19 ]. Survey timing may have been different among PGY 1’s, 2’s and 3’s, accounting in part for some differences. Furthermore, worklife and wellness may vary considerably throughout the year; this variation is not accounted for by our analyses. As for strengths, survey items and the Mini ReZ are well validated [ 10 , 11 ], and mixed methods provide confirmation and enhancement of factors facing residents; furthermore, the data are reasonably current, as of late April 2023. This lends both urgency and temporal validity to the findings.

Conclusions/implications

Residents perceive strong support by staff, peers and clinical teams. However, burnout rates still exceed 40% nationally, and are higher among females and PGY 2’s. Addressing workload, EMR use, sleep impairment and chaotic environments, as well as providing clear recognition of resident efforts, are evidence-based strategies to pursue for burnout reduction. Future studies could measure the impact of interventions, time spent on varied aspects of work and care, and mechanisms to better reach those not identifying race or gender.

Availability of data and materials

The data that support the findings of this study are provided from the American Medical Association. Restrictions apply to the availability of these data, which were obtained through individual data use agreements from engaged residency programs, and thus are not publicly available. An aggregate, de-identified data set can be obtained from the corresponding author upon reasonable request.

Abbreviations

American Medical Association

Adjusted Odds Ratio

Electronic Medical Record.

Effect Size

False Discovery Rate

Post Graduate Year

Prefer not to indicate gender or race

Maslach Burnout Inventory

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ML Role: lead author, analyzed data, wrote paper. SM Role: qualitative analysis; paper review. PS Role: performed data analysis and retrieval. AB Role: critical review of paper, emphasizing impact on residents and gender differences. NN Role: oversees data collection, reviewed findings, critiqued the paper. SP Role: data review, manuscript review, referencing. SKP Role: part of team that collects data; reviewed the manuscript and provided comments. CN Role: part of team that collects data; reviewed the manuscript and provided comments. CAS Role: involved in data review, writing the paper, critical review. EG Role: involved in data collection, hypothesis generation, data review, manuscript review. MS Role: involved in data collection, hypothesis generation, data review, manuscript review. MA. Role: data interpretation and manuscript review, especially focused on gender differences in burnout. EES Role: qualitative analysis, data review, manuscript writing and review. RB Role: statistical analyses, data review, manuscript writing and review.

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Dr. Linzer and Ms. Poplau are supported for this work by the AMA, and receive support from the IHI and Optum Office for Provider Advancement for burnout reduction studies. Dr. Goelz and Dr. Stillman are supported by the AMA for this work; Dr. Goelz is also supported for burnout reduction work by IHI and Dr. Stillman is supported for burnout studies by Optum. Dr. Brown was supported by the AMA for this work. Dr. Sinsky, Ms. Nankivil, Ms. Patel, Ms. Nosah and Ms. Shah all work for the AMA. The other authors declare no conflicts.

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Linzer, M., Mallick, S., Shah, P. et al. Resident worklife and wellness through the late phase of the pandemic: a mixed methods national survey study. BMC Med Educ 24 , 484 (2024). https://doi.org/10.1186/s12909-024-05480-5

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Enhancing Water Quality: A Rainwater and Reclaimed Water Sediment Filtration System

• Eric Brondo
• Mark Christian Aringo
• Marlon Hernandez
• Jamaica Hernandez
• May 8, 2024
• Estate Management

Eric Brondo 1 , Mark Christian Aringo 1 , Marlon Hernandez 1 , Jamaica Hernandez 2

1 Bachelor of Industrial Technology Department, Bulacan State University Sarmiento Campus

2 Information Technology and Data Science Department, Bulacan State University Sarmiento Campus

DOI: https://dx.doi.org/10.47772/IJRISS.2024.804084

Received: 20 March 2024; Accepted: 04 April 2024; Published: 08 May 2024

This research explores the development of a novel water filtration system. Using a mix of online and direct surveys, the study reveals strong support for addressing water supply challenges in specific areas of the campus. It introduces an innovative approach centered on recycling excess water and rainwater, which has broad applications beyond the campus, benefiting both humans and the environment. Preliminary results highlight the importance of implementing this “Every Drip Filtration System” on campus, emphasizing its potential to conserve water. Furthermore, the study underscores the universal value of water recycling and offers a versatile solution for daily living. In conclusion, this research holds immense promise for public and private sectors, advocating for water conservation and alternative water sources, benefiting both society and the environment.

Keywords: Water, Filtration System, Rainwater Recycling, Water Conservation, Arduino

INTRODUCTION

Worldwide, there are an estimated 2.3 billion people living in water-scarce and stressed areas. The water in these areas may contain harmful pathogens, such as bacteria, that can have a negative effect on human health [8].

Water, as vital as the air we breathe, is an indispensable element of human survival [1]. However, our journey through history reveals a recurring challenge – the purification of water. This narrative transcends mere adversity; it is a testament to humanity’s resolute spirit in securing a lifeline for our existence [1]. Across epochs, water-related predicaments have relentlessly tested our ingenuity, and each time, innovation has risen to the occasion.

Today, the scope of our predicament is impossible to ignore. As the global population burgeons, so does the demand for water, magnifying a litany of water-related crises [1]. Each year, the number of water consumers’ surges, and the repercussions are felt worldwide [2]. Access to clean, safe water becomes precarious for some, permeating our daily lives with associated challenges. Within this pressing context, ingenious minds strive to develop solutions that not only ensure our survival but also alleviate mounting water woes.

At the heart of this endeavor lies water filtration, a beacon of hope. It promises to recycle water and purify contaminated sources, offering a lifeline to parched communities [1]. Remote regions and distant provinces often bear the brunt of water scarcity, making this technology crucial [3]. Forward-thinking institutions like Super Markets (SM) have already embraced water recycling as a practical solution to reduce water consumption, particularly in restroom facilities.

Filtration is one of the core processes in water treatment. The term refers to the removal, mainly by physical action, of suspended solids as the suspension flows through a bed packed by granular media [6].  Filtration is the removal of suspended and colloidal particles present in an aqueous suspension that drains through a porous medium [7].

This narrative brings us to the province of Bulacan, where a unique opportunity awaits. Here, we aspire to implement a comprehensive water filtration system capable of reshaping our water consumption patterns dramatically. This visionary system, currently under development, seeks to reclaim bathroom and sink wastewater while harvesting precious rainwater. As envisioned, every respondents and staff will actively participate in water conservation [9]. The outcome will be recycled water [10], specifically designated for toilet flushing, ushering in an era of conscientious water stewardship. The rewards extend beyond utility cost savings; they encompass a profound contribution to our environment’s preservation.

This research seeks to confront head-on the formidable water-related challenges that plague our world. It underscores the significance of our actions today in shaping the water landscape of tomorrow.

METHODOLOGY

The study is dedicated to the development of an innovative water filtration device. This system incorporates a range of materials, including Activated Charcoal, Fine and Coarse Silica Sand, Gravel, Filter Foam, and Filter screen, each chosen for its unique ability to effectively eliminate even the smallest particles and bacteria from water, rendering it clean and reusable [4].

The creation of a prototype device for this endeavour serves as a tangible demonstration of the project’s potential. It showcases the transformation of untreated water into clear and clean water as it passes through the filtration device. Additionally, the inclusion of a Total Dissolved Solids (TDS) meter allows for precise quantification of remaining small particles in the filtered water [5].

The methodology of this research encompasses several crucial steps. First, the selection of the area as the research site serves to evaluate the water efficiency of its restroom facilities. The research aims to involve 50 diverse respondents, encompassing varying genders and occupations. To ensure an unbiased selection, the study employs stratified sampling techniques, which also involve participants in recruiting other potential subjects [4]. Participants include respondents from Bulacan and staff who regularly utilize the campus’s restroom facilities.

Quantitative research results will be derived from meticulous data organization, examination, and analysis of responses gathered from targeted respondents. This involves the active participation of 50 users of the public restrooms at Bulacan, who responded to questionnaires regarding the impact of rain and water filtration systems. Additionally, the research outcomes will include an evaluation and assessment of each survey result by the research team.

The sample size calculation was based on the proportional sampling formula:

nh = (Nh/N) * n

nh = sample size using proportionate stratified random sampling

Nh = total stratum population

N = total population

n = sample size

For this study, the population of Personnel was 98, and the population of for selected areas was 4,488. The resulting sample sizes are as follows:

For chosen respondents:

nh = (98 + 4488) * 40

Number of chosen student respondents = 40

For chosen Personnel respondents:

nh = (98 + 4488) * 10

Number of chosen Personnel respondents = 10

The researchers adopted a quantitative approach and employed survey questionnaires that incorporated descriptive and rating scale elements for data collection. These questionnaires were administered to 50 participants from Bulacan. Their responses were collected to assess the impact of rain and the utilization of water filtering equipment in the campus’s public restrooms.

The primary objectives of that research study were to increase awareness of the value of water in everyone’s lives, encourage responsible water consumption, and identify potential consequences of consumer behaviour. The researchers surveyed 50 individuals from Bulacan, consisting of 40 respondents from different ages, and ten staff, including security guards and utility workers. The responders to the initial survey were profiled based on their roles, distinguishing between respondents and staff members.

Material for Water Pump

2 pcs ¾ blue Pipe

Male Adaptor

¾ Pipe with ¾ threaded tee

 ½ Pip e (used for the Pressure)

Pressure Gauge

Pressure Switch

 ¾ Pipe with ¾ Ball Valve

¾ Check valve

¾ Blue Pipe (connected to the water tank)

¾ Blue Pipe (connected to the water filtration)

Assembly Steps for Water Pump Installation:

Begin by using two male adapters (2 pieces). Attach one male adapter to the upper side and the other to the right side of the water pump.

Utilize two pieces of ¾-inch pipes. Connect one of the ¾-inch pipes to the right side of the water pump; this will serve as the water inlet (Step 1). The second pipe will be connected to the upper part of the water pump and will function as the water outlet (Step 2).

Employ a ¾-inch tee fitting. Attach the tee fitting to the ¾-inch pipe from the water pump’s outlet. Subsequently, connect a ¾-inch ball valve to the tee fitting; this valve regulates the water pump’s pressure (Step 3).

Integrate another ¾-inch pipe and connect it to the tee fitting, originating from the water pump’s outlet (Step 4).

Employ three threaded tees, each measuring ¾-inch. Begin by connecting the first threaded tee to the ¾-inch pipe (Step 4); this tee will link to a ¾-inch pipe, which serves as an alternative to the pressure tank. The second threaded tee will be used to connect a pressure gauge (Step 5), while the third threaded tee will facilitate the connection of a pressure switch (Step 6).

Add a ¾-inch 45°-degree elbow (Step 7) to the ¾-inch pipe (Step 4).

Connect another ¾-inch pipe (Step 8) with a ¾-inch ball valve to the ¾-inch 45°-degree elbow (Step 7).

Connect the ¾-inch pipe (8) to the water tank (9).

Attach a ¾-inch 45°-degree elbow (10) to the pipe (1) originating from the water pump’s inlet.

Extend the assembly by connecting another ¾-inch pipe (11) to the previously attached ¾-inch 45°-degree elbow.

Install a ¾-inch check valve (12) at the lower end of the ¾-inch pipe (11), ensuring it is connected to the water tank, which serves as the storage for filtered water.

Connect another ¾-inch pipe (14) equipped with a ¾-inch ball valve to both the filtration device and the water tank (storage of filtered water) (13).

Link a ¾-inch pipe (15) to an additional water storage unit. This pipe should connect to a ¾-inch 45°-degree elbow. Extend this assembly by adding another ¾-inch pipe and securing it with a ¾-inch ball valve. Finally, connect this pipe to the water filtration device.

RESULT AND DISCUSSION

This comprehensive study employed a dual methodology, comprising online surveys and direct surveys, engaging selected participants representing a diverse cross-section of students and campus personnel. The survey outcomes resoundingly affirmed strong support for the installation of a filtration system within the campus, effectively addressing critical water supply challenges encountered in specific staff and student comfort facilities.

This study’s innovative approach centres on the recycling of excess water and rainwater, unveiling its substantial potential in significantly reducing overall water consumption. Beyond the campus, the implications of these findings extend to various settings, including public and private schools, hotels, residential neighbourhoods, and regions grappling with water scarcity or inadequate water supply. This pioneering strategy not only augments the well-being of human populations but also ushers in positive ramifications for animal welfare and the broader environment.

The preliminary survey results from selected respondents underscore the paramount significance of implementing the proposed “Every Drip Filtration System” innovation within Bulacan. Collecting and analyzing feedback and opinions from respondents have illuminated the primary effects of this system within the campus context. By proffering an alternative water supply and fostering water conservation practices within the campus precincts, this research underscores the profound significance and potential transformative impact of a filtration system.

Moreover, the research study investigating the rain and used water filtration system underscores the universal value of recycling. Implementing such a device holds substantial promise, especially in rural areas where water supplies are often limited. A filtration system emerges as an adaptable and versatile solution for daily living, facilitating the recycling of used water and rainwater for diverse purposes, including flushing, cleaning, gardening, and more, contingent upon the quality of the filtered water.

In conclusion, the findings of this research study, coupled with the implementation of the project hold immense promise for both public and private sectors. This research project serves as an educational platform, advocating for water recycling, the utilization of filtered water, and the provision of alternative water sources for multifarious applications. While such technology might be prevalent in certain regions, its overarching significance resides in its capacity to curtail water consumption and safeguard this invaluable resource, thereby bestowing profound benefits upon society and the environment at large.

ACKNOWLWDGEMENT

The authors would like to acknowledge Bulacan State University for guiding us thru this research endeavor. The Bachelor of Industrial Technology Department of BulSU Sarmiento Campus for the encouragement.

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• Sharma, A. K., Tjandraatmadja, G., & Chanan, A. (2013). Rainwater harvesting: A sustainable solution to drinking water crisis in rural areas of developing countries. Procedia Environmental Sciences, 18, 2-10.
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• Cescon, A., & Jiang, J. (2020). Filtration process and alternative filter media material in water treatment. Water, 12(12), 3377. https://doi.org/10.3390/w12123377
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