Grad Coach

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

  • What is the golden thread
  • What are research aims ( examples )
  • What are research objectives ( examples )
  • What are research questions ( examples )
  • The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Free Webinar: How To Find A Dissertation Research Topic

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

what is research aims and objectives

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

The research objectives detail the specific steps that you, as the researcher, will take to achieve the research aims you laid out.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

what is research aims and objectives

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

Narrative analysis explainer

38 Comments

Isaac Levi

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Hatimu Bah

Well appreciated. This has helped me greatly in doing my dissertation.

Dr. Abdallah Kheri

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

Ekwunife, Chukwunonso Onyeka Steve

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Tosin

Thanks so much. This was really helpful.

Ishmael

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

sylas

i found this document so useful towards my study in research methods. thanks so much.

Michael L. Andrion

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Scarlett

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

Enoch Tindiwegi

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

Sora Dido Boru

This is quite helpful. I really appreciate!

Chulyork

The article made it simple for researcher students to differentiate between three concepts.

Afowosire Wasiu Adekunle

Very innovative and educational in approach to conducting research.

Sàlihu Abubakar Dayyabu

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

Mohammed Shamsudeen

A very helpful piece. thanks, I really appreciate it .

Sonam Jyrwa

Very well explained, and it might be helpful to many people like me.

JB

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

UN

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

Derek Jansen

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Saen Fanai

Exactly what I need in this research journey, I look forward to more of your coaching videos.

Abubakar Rofiat Opeyemi

This helped a lot. Thanks so much for the effort put into explaining it.

Lamin Tarawally

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

Latifat Muhammed

This is quite useful thanks

Yetunde

I’m excited and thankful. I got so much value which will help me progress in my thesis.

Amer Al-Rashid

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Webby

Very helpful and important tips on Aims, Objectives and Questions.

Refiloe Raselane

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Annabelle Roda-Dafielmoto

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

Joe

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Abdella

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Sheikh

Well explained

New Growth Care Group

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

yaikobe

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

UMAR SALEH

I really found these tips helpful. Thank you very much Grad Coach.

Rahma D.

I found this article helpful. Thanks for sharing this.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

  • Print Friendly

Research-Methodology

Formulating Research Aims and Objectives

Formulating research aim and objectives in an appropriate manner is one of the most important aspects of your thesis. This is because research aim and objectives determine the scope, depth and the overall direction of the research. Research question is the central question of the study that has to be answered on the basis of research findings.

Research aim emphasizes what needs to be achieved within the scope of the research, by the end of the research process. Achievement of research aim provides answer to the research question.

Research objectives divide research aim into several parts and address each part separately. Research aim specifies WHAT needs to be studied and research objectives comprise a number of steps that address HOW research aim will be achieved.

As a rule of dumb, there would be one research aim and several research objectives. Achievement of each research objective will lead to the achievement of the research aim.

Consider the following as an example:

Research title: Effects of organizational culture on business profitability: a case study of Virgin Atlantic

Research aim: To assess the effects of Virgin Atlantic organizational culture on business profitability

Following research objectives would facilitate the achievement of this aim:

  • Analyzing the nature of organizational culture at Virgin Atlantic by September 1, 2022
  • Identifying factors impacting Virgin Atlantic organizational culture by September 16, 2022
  • Analyzing impacts of Virgin Atlantic organizational culture on employee performances by September 30, 2022
  • Providing recommendations to Virgin Atlantic strategic level management in terms of increasing the level of effectiveness of organizational culture by October 5, 2022

Figure below illustrates additional examples in formulating research aims and objectives:

Formulating Research Aims and Objectives

Formulation of research question, aim and objectives

Common mistakes in the formulation of research aim relate to the following:

1. Choosing the topic too broadly . This is the most common mistake. For example, a research title of “an analysis of leadership practices” can be classified as too broad because the title fails to answer the following questions:

a) Which aspects of leadership practices? Leadership has many aspects such as employee motivation, ethical behaviour, strategic planning, change management etc. An attempt to cover all of these aspects of organizational leadership within a single research will result in an unfocused and poor work.

b) An analysis of leadership practices in which country? Leadership practices tend to be different in various countries due to cross-cultural differences, legislations and a range of other region-specific factors. Therefore, a study of leadership practices needs to be country-specific.

c) Analysis of leadership practices in which company or industry? Similar to the point above, analysis of leadership practices needs to take into account industry-specific and/or company-specific differences, and there is no way to conduct a leadership research that relates to all industries and organizations in an equal manner.

Accordingly, as an example “a study into the impacts of ethical behaviour of a leader on the level of employee motivation in US healthcare sector” would be a more appropriate title than simply “An analysis of leadership practices”.

2. Setting an unrealistic aim . Formulation of a research aim that involves in-depth interviews with Apple strategic level management by an undergraduate level student can be specified as a bit over-ambitious. This is because securing an interview with Apple CEO Tim Cook or members of Apple Board of Directors might not be easy. This is an extreme example of course, but you got the idea. Instead, you may aim to interview the manager of your local Apple store and adopt a more feasible strategy to get your dissertation completed.

3. Choosing research methods incompatible with the timeframe available . Conducting interviews with 20 sample group members and collecting primary data through 2 focus groups when only three months left until submission of your dissertation can be very difficult, if not impossible. Accordingly, timeframe available need to be taken into account when formulating research aims and objectives and selecting research methods.

Moreover, research objectives need to be formulated according to SMART principle,

 where the abbreviation stands for specific, measurable, achievable, realistic, and time-bound.

Examples of SMART research objectives

At the conclusion part of your research project you will need to reflect on the level of achievement of research aims and objectives. In case your research aims and objectives are not fully achieved by the end of the study, you will need to discuss the reasons. These may include initial inappropriate formulation of research aims and objectives, effects of other variables that were not considered at the beginning of the research or changes in some circumstances during the research process.

Research Aims and Objectives

John Dudovskiy

  • Privacy Policy

Buy Me a Coffee

Research Method

Home » Research Objectives – Types, Examples and Writing Guide

Research Objectives – Types, Examples and Writing Guide

Table of Contents

Research Objectives

Research Objectives

Research objectives refer to the specific goals or aims of a research study. They provide a clear and concise description of what the researcher hopes to achieve by conducting the research . The objectives are typically based on the research questions and hypotheses formulated at the beginning of the study and are used to guide the research process.

Types of Research Objectives

Here are the different types of research objectives in research:

  • Exploratory Objectives: These objectives are used to explore a topic, issue, or phenomenon that has not been studied in-depth before. The aim of exploratory research is to gain a better understanding of the subject matter and generate new ideas and hypotheses .
  • Descriptive Objectives: These objectives aim to describe the characteristics, features, or attributes of a particular population, group, or phenomenon. Descriptive research answers the “what” questions and provides a snapshot of the subject matter.
  • Explanatory Objectives : These objectives aim to explain the relationships between variables or factors. Explanatory research seeks to identify the cause-and-effect relationships between different phenomena.
  • Predictive Objectives: These objectives aim to predict future events or outcomes based on existing data or trends. Predictive research uses statistical models to forecast future trends or outcomes.
  • Evaluative Objectives : These objectives aim to evaluate the effectiveness or impact of a program, intervention, or policy. Evaluative research seeks to assess the outcomes or results of a particular intervention or program.
  • Prescriptive Objectives: These objectives aim to provide recommendations or solutions to a particular problem or issue. Prescriptive research identifies the best course of action based on the results of the study.
  • Diagnostic Objectives : These objectives aim to identify the causes or factors contributing to a particular problem or issue. Diagnostic research seeks to uncover the underlying reasons for a particular phenomenon.
  • Comparative Objectives: These objectives aim to compare two or more groups, populations, or phenomena to identify similarities and differences. Comparative research is used to determine which group or approach is more effective or has better outcomes.
  • Historical Objectives: These objectives aim to examine past events, trends, or phenomena to gain a better understanding of their significance and impact. Historical research uses archival data, documents, and records to study past events.
  • Ethnographic Objectives : These objectives aim to understand the culture, beliefs, and practices of a particular group or community. Ethnographic research involves immersive fieldwork and observation to gain an insider’s perspective of the group being studied.
  • Action-oriented Objectives: These objectives aim to bring about social or organizational change. Action-oriented research seeks to identify practical solutions to social problems and to promote positive change in society.
  • Conceptual Objectives: These objectives aim to develop new theories, models, or frameworks to explain a particular phenomenon or set of phenomena. Conceptual research seeks to provide a deeper understanding of the subject matter by developing new theoretical perspectives.
  • Methodological Objectives: These objectives aim to develop and improve research methods and techniques. Methodological research seeks to advance the field of research by improving the validity, reliability, and accuracy of research methods and tools.
  • Theoretical Objectives : These objectives aim to test and refine existing theories or to develop new theoretical perspectives. Theoretical research seeks to advance the field of knowledge by testing and refining existing theories or by developing new theoretical frameworks.
  • Measurement Objectives : These objectives aim to develop and validate measurement instruments, such as surveys, questionnaires, and tests. Measurement research seeks to improve the quality and reliability of data collection and analysis by developing and testing new measurement tools.
  • Design Objectives : These objectives aim to develop and refine research designs, such as experimental, quasi-experimental, and observational designs. Design research seeks to improve the quality and validity of research by developing and testing new research designs.
  • Sampling Objectives: These objectives aim to develop and refine sampling techniques, such as probability and non-probability sampling methods. Sampling research seeks to improve the representativeness and generalizability of research findings by developing and testing new sampling techniques.

How to Write Research Objectives

Writing clear and concise research objectives is an important part of any research project, as it helps to guide the study and ensure that it is focused and relevant. Here are some steps to follow when writing research objectives:

  • Identify the research problem : Before you can write research objectives, you need to identify the research problem you are trying to address. This should be a clear and specific problem that can be addressed through research.
  • Define the research questions : Based on the research problem, define the research questions you want to answer. These questions should be specific and should guide the research process.
  • Identify the variables : Identify the key variables that you will be studying in your research. These are the factors that you will be measuring, manipulating, or analyzing to answer your research questions.
  • Write specific objectives: Write specific, measurable objectives that will help you answer your research questions. These objectives should be clear and concise and should indicate what you hope to achieve through your research.
  • Use the SMART criteria: To ensure that your research objectives are well-defined and achievable, use the SMART criteria. This means that your objectives should be Specific, Measurable, Achievable, Relevant, and Time-bound.
  • Revise and refine: Once you have written your research objectives, revise and refine them to ensure that they are clear, concise, and achievable. Make sure that they align with your research questions and variables, and that they will help you answer your research problem.

Example of Research Objectives

Examples of research objectives Could be:

Research Objectives for the topic of “The Impact of Artificial Intelligence on Employment”:

  • To investigate the effects of the adoption of AI on employment trends across various industries and occupations.
  • To explore the potential for AI to create new job opportunities and transform existing roles in the workforce.
  • To examine the social and economic implications of the widespread use of AI for employment, including issues such as income inequality and access to education and training.
  • To identify the skills and competencies that will be required for individuals to thrive in an AI-driven workplace, and to explore the role of education and training in developing these skills.
  • To evaluate the ethical and legal considerations surrounding the use of AI for employment, including issues such as bias, privacy, and the responsibility of employers and policymakers to protect workers’ rights.

When to Write Research Objectives

  • At the beginning of a research project : Research objectives should be identified and written down before starting a research project. This helps to ensure that the project is focused and that data collection and analysis efforts are aligned with the intended purpose of the research.
  • When refining research questions: Writing research objectives can help to clarify and refine research questions. Objectives provide a more concrete and specific framework for addressing research questions, which can improve the overall quality and direction of a research project.
  • After conducting a literature review : Conducting a literature review can help to identify gaps in knowledge and areas that require further research. Writing research objectives can help to define and focus the research effort in these areas.
  • When developing a research proposal: Research objectives are an important component of a research proposal. They help to articulate the purpose and scope of the research, and provide a clear and concise summary of the expected outcomes and contributions of the research.
  • When seeking funding for research: Funding agencies often require a detailed description of research objectives as part of a funding proposal. Writing clear and specific research objectives can help to demonstrate the significance and potential impact of a research project, and increase the chances of securing funding.
  • When designing a research study : Research objectives guide the design and implementation of a research study. They help to identify the appropriate research methods, sampling strategies, data collection and analysis techniques, and other relevant aspects of the study design.
  • When communicating research findings: Research objectives provide a clear and concise summary of the main research questions and outcomes. They are often included in research reports and publications, and can help to ensure that the research findings are communicated effectively and accurately to a wide range of audiences.
  • When evaluating research outcomes : Research objectives provide a basis for evaluating the success of a research project. They help to measure the degree to which research questions have been answered and the extent to which research outcomes have been achieved.
  • When conducting research in a team : Writing research objectives can facilitate communication and collaboration within a research team. Objectives provide a shared understanding of the research purpose and goals, and can help to ensure that team members are working towards a common objective.

Purpose of Research Objectives

Some of the main purposes of research objectives include:

  • To clarify the research question or problem : Research objectives help to define the specific aspects of the research question or problem that the study aims to address. This makes it easier to design a study that is focused and relevant.
  • To guide the research design: Research objectives help to determine the research design, including the research methods, data collection techniques, and sampling strategy. This ensures that the study is structured and efficient.
  • To measure progress : Research objectives provide a way to measure progress throughout the research process. They help the researcher to evaluate whether they are on track and meeting their goals.
  • To communicate the research goals : Research objectives provide a clear and concise description of the research goals. This helps to communicate the purpose of the study to other researchers, stakeholders, and the general public.

Advantages of Research Objectives

Here are some advantages of having well-defined research objectives:

  • Focus : Research objectives help to focus the research effort on specific areas of inquiry. By identifying clear research questions, the researcher can narrow down the scope of the study and avoid getting sidetracked by irrelevant information.
  • Clarity : Clearly stated research objectives provide a roadmap for the research study. They provide a clear direction for the research, making it easier for the researcher to stay on track and achieve their goals.
  • Measurability : Well-defined research objectives provide measurable outcomes that can be used to evaluate the success of the research project. This helps to ensure that the research is effective and that the research goals are achieved.
  • Feasibility : Research objectives help to ensure that the research project is feasible. By clearly defining the research goals, the researcher can identify the resources required to achieve those goals and determine whether those resources are available.
  • Relevance : Research objectives help to ensure that the research study is relevant and meaningful. By identifying specific research questions, the researcher can ensure that the study addresses important issues and contributes to the existing body of knowledge.

About the author

' src=

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Paper Citation

How to Cite Research Paper – All Formats and...

Data collection

Data Collection – Methods Types and Examples

Delimitations

Delimitations in Research – Types, Examples and...

Research Paper Formats

Research Paper Format – Types, Examples and...

Research Process

Research Process – Steps, Examples and Tips

Research Design

Research Design – Types, Methods and Examples

Mon - Sat 9:00am - 12:00am

  • Get a quote

A Complete Guide to Write Research Aims and Objectives

The importance of making a good quality aim and objectives of a research is paramount in the success of the research.

In this post, you will learn:

  • What is research aim?
  • What are research objectives?
  • How to differentiate between research aim and research objective
  • Examples of research aims and objectives
  • Key points to remember while writing research aim and objectives
  • Do’s and Don’ts

What Is Research Aim?

The research aim is the primary focus of the research and determines what the research serves to do. It defines the purpose of the research and tells the audience what the research aims to achieve.

Because research aims are so important for the study, a sun heading in the introductory chapter is usually dedicated to them. They are written in a paragraph form and define the main purpose of conducting the research on a topic.

Example of a good quality research aim

Research about the effects of climate change on the mental health of young adults can be worded as follows:

“The effects of climate change information on the minds of young adults are under researched. This research aims to find the effects that climate change information has on the mental health of young adults. By studying the effects of the intensity and frequency of the consumption of climate change news and forecasts among young adults, this study aims to see how climate change information is influencing their mental health.”

The above research aim is focused and clear and presents the reader with a clear understanding of the purpose of the research.

What Is Research Objective?

Research aims are related to research objectives. The research aim determines the overall purpose of the study, and the objectives determine in what ways that purpose will be achieved. The purpose of the research aim is separated into subsections. However, If any you need to order IT Research Paper help services then you have to take a survey on the net. These subsections are smaller steps that define the objectives of the research.

Research objectives are usually written in the form of a list. These small bits of steps can be checked off as the research progresses. They are written in chronological order, starting with the first objective that needs to be achieved and ending with the final one.

Example of research objectives

Taking the example of the research aim above, we can divide it into smaller sections to create specific aims of the research.

  • Understand the concepts of climate change and mental health
  • Understand the relationship between climate change and mental health
  • Determine the frequency and intensity of climate change news consumption among young adults
  • Determine the frequency and intensity of news affects the mental health of young adults
  • Develop recommendations for clinical practice in the field

From this example, you can see how the research aim was broken down into smaller, specific objectives that were then listed down.

Differentiate Research Between Aims & Objectives

Although the two concepts are related, they are not the same. The differences between research aim and research objectives are:

  • The way they are worded are different: Research aim is worded in a wide scoped way, while research objectives are worded as specific, narrowed down tasks.
  • The focus of the two are different: research aim focuses on the overall purpose while research objectives focus on how to achieve that purpose.
  • The purpose of the two are different: research aims are concerned with the overall findings of a research, which are long-term, while research objectives serve to define the short term aims of the research.
  • The way they are presented are different: research aims are written in a small paragraph form while the objectives are written in the form of a list.

3 Key Points To Remember While Drafting Research Aim

The ways of writing a research aim varies with the researcher, but there are certain points to keep in mind to write a good quality research aim:

1. Answer the “why” question of the research: A research aim needs to provide an answer for why the study is being conducted. It needs to describe, in a small sentence or phares, why the research is important to conduct.

Taking the example of the research aim above, we can see that it answers the why question:

“The effects of climate change information on the minds of young adults are under researched”.

2. Answer the “what” question of the research: this is the main purpose of the research aim, as it signifies the main aim of the research.

From the example above, the “what” question is answered as follows:

“This research aims to find the effects that climate change information has on the mental health of young adults.”

3. Lastly, the research aim needs to answer the “how” question. In a simple sentence or a few phrases, it should outline the main way in which you are planning to achieve the aim.

From the example above, the “how” question is answered as follows:

“By studying the effects of the intensity and frequency of the consumption of climate change news and forecasts among young adults, this study aims to see how climate change information is influencing their mental health.”

Checklist of research aim:

  • Tells the purpose of research
  • Tells why the research is important
  • Tells how the aim will be achieved
  • Is clear and concise

5 Key Points To Remember While Drafting Research Objective

An easy way to determine the quality of your research objectives is to apply the SMART method to them:

  • You need to make sure that the objectives are worded as specific tasks you need to achieve. They should not be vague.
  • You should ensure that the objectives are measurable, meaning that they allow you to see how much of them have been achieved and how much are left to work on.
  • The objectives need to be achievable, meaning you should have the resources to work on them.
  • They need to be relevant to getting to your research aim.
  • They need to be achievable in the time you have available for your research project.

In the example above the objectives follow the above mentioned criteria. While making your own objectives, make sure to evaluate them using the points above to ensure your objectives are good quality.

Checklist of research objectives

Do’s and Don’ts of research aims and objectives

What to do and what to avoid in writing aims and objectives

The Takeaway

  • The research aim is the primary focus of the research.
  • Research aims are related to research objectives
  • The objectives determine in what ways that purpose will be achieved.
  • Research aim needs to answer the “what”, “why” and “how” questions of the research.
  • Research objectives need to be SMART.

Power Point      Google Slides

Blogging has been my favourite part-time ever since. However, after graduating from high school, I chose to blog, as my career. It has been 5 years and I am here, blogging my heart out. Writing is the best way to creatively mention your thoughts, and I am a creativity expert!.

It is Time to Boost Your Grades with Professional Help

Improved scores.

Get Better Grades in Every Subject

Timely Delivery

Submit Your Assignment on Time

Experienced Writers

Trust Academic Experts Based in UK

Safety is Assured

Your Privacy is Our Topmost Concern

Hire a Writer

Subject* Accounting Accounts Law Advertising Aeronautical Engineering Agency Law Agriculture Animal Management Anthropology Archaeology Architecture Art Astrophysics Biochemistry Biology Biotechnology Business Chemical Engineering Chemistry Child Care Civil Engineering Civil Litigation Law Classics Commercial Law Commercial Property Law Communications Company Law / Business Law Comparative Law Computer Engineering Computing Constitutional / Administrative Law Consumer Law Contract Law Criminal Law Criminal Litigation Criminology Crisis Management Cultural Studies Design Drama E-Commerce Econometrics Economics Education Electrical Engineering Electronic Engineering Employment Law Engineering English Language Environmental Studies Equity Law Estate Management European Law European Studies Events Management Family Law Fashion Film Studies Finance Finance Law Forensic Science General Law Genetics Geography Geology History Hospitality Housing Housing Law HRM Human Rights I.T. Immigration Law Information Systems Intellectual Property Law International Development International Law International Relations International Studies Journalism Jurisprudence Land Law / Property Law Landlord and Tenant Law Languages Law Leisure Management Linguistics Literature Management Maritime Law Marketing Materials Science Mathematics Mechanical Engineering Mechanics Media Media and Information Technology Law Medicine Mental Health Law Midwifery Military Multimedia Negligence Law Neuroscience Nursing Nutrition Operations Management Oriental Studies Pathology Pharmacology Philosophy Physical Education Physical_Sciences Physics Planning / Environmental Law Plant Science Politics Product Design Professional Conduct Law Project Management Property Psychology Public Law Quantitative Methods Religion Restitution Law Risk Management Sciences Shipping Policy Social Work Sociology Software Engineering Sports Statistics Strategic Management Succession Law Surveying Tax Law Teaching Television Theatre Theology Tort Law Tourism Trusts Law Urban Studies Veterinary Wills / Probate Law Zoology Bio-informatics Biomedical Sciences Computer Forensics Data Mining Dentistry Engineering Business Management Environmental Engineering Environmental Management Environmental Science Epidemiology Geophysics Health and Safety Management Occupational Psychology Physiotherapy Public Health Real Estate Research Methods Security Studies Shipping and Trade Finance Sports and Exercise Science Supply Chain Management Sustainable Energy Telecommunication Engineering Toxicology Public Relations Other

Paper Type* Dissertation Dissertation Topics Dissertation-Abstract Dissertation Proposal Dissertation- Analysis Chapter Dissertation- Conclusion Chapter Dissertation- Introduction Chapter Dissertation- Literature Review Chapter Dissertation- Methodology Chapter Dissertation Editing and Proof Reading Essay Admission Essay Scholarship Essay Case Study Annotated Bibliography Assignment Book Report/Review Case Analysis Course Work Information and Communication/ Computer Technology Reaction Paper Research Paper Research Proposal Statistics Project Term Paper Thesis Thesis Proposal Laboratory Report Movie Review Multiple Choice Questions Power Point Presentation Article Speech Other

Education Under Graduate Graduate Masters PhD

LOGO

A Guide to Writing Research Objectives and Aims

  • Post author: Research Zone
  • Post published: September 6, 2021
  • Post category: Academic Advice / Writing Thesis
  • Post comments: 1 Comment

A Guide to Writing Research Objectives and Aims

Introduction

In determining the success of your research project, it is crucial to understand your research objectives and aim. However, it is, unfortunately, an aspect that many students struggle with, resulting in poor performance. As a result of their importance, if you suspect even the slightest possibility that you belong to this group of students, we strongly recommend that you read this article in its entirety.

In this article, we describe what research aim and objectives are, what distinguishes them from each other, and how to write them correctly.

What is Research Aim?

Research aims describe the purpose or main goal of a project. It help your reader to understand the focus of your research and gives them an idea of what you are trying to accomplish. Research aims are found in their own subsection under the introduction section of all research documents, regardless of whether they are dissertations or research papers.

In most situations, a research aim is expressed as a broad statement of the main goal of the study and doesn’t need to be more than one sentence. The exact format of the outline will vary depending on your preference, but it should all explain the purpose (context), your objective (the actual aim), and how you plan to accomplish it (highlights of your objectives).

What are Research Objectives?

Research aim defines what your study is going to answer, but research objectives outline how it’s going to answer it.

Those objectives break down each component of your research project into smaller portions, each representing a key section of it. Thus, almost all dissertations and theses are organized into numbered lists, with each item getting its own chapter.

Understanding the difference between research objectives and aim

The above explanation should make clear the difference between aim and objectives, but to clarify:

  • Aim focus on what a project proposes to achieve; objectives focus on how the project will achieve its goal.
  • The research objectives are more specific than the research aims.
  • Objectives focus on the short-term and immediate outcomes of a project while aim focus on its long-term outcomes.
  • It would be best to write an objective as a numbered list; research aim can be written in one sentence or short paragraph.

How to Write the Aim and Objectives of Research?

It is important to note that there is no definitive way to write clear objectives and aim for research. Researchers typically formulate their goals and objectives in so many different ways, and your supervisor may often influence the formulation based on their preferences.

Nevertheless, there are a few basic principles you should observe to ensure good practice; these principles are listed below.

Research Aim

The aim should include three parts, which address the following questions:

  • Why is this research necessary?
  • What is the purpose of this study?
  • How will you accomplish it?

It is easier to accomplish writing your research aim by addressing each question using its own sentence, although you can combine sentences for each or write multiple sentences for each question, the important thing is to address each one individually.

The first question, why, provides context to your research project, the second question, what describes the aim of your research, and the last question, how, acts as an introduction to your objectives which will immediately follow.

Research Objectives

Each of your research objectives should have the following:

  • Specific: Is the action you intend to take unclear, or is it focused and well-defined?
  • Assessable: What method will you use to measure your progress and determine when you have accomplished your goal?
  • Obtainable: Do you have the necessary support, resources, and facilities to execute the project?
  • Relevant: Does the action you propose support the achievement of your research aim?
  • Timebound: Are you realistically able to complete the action within the given timeframe?

Properly formulating the aims and objectives of your thesis, dissertation, or research paper is an integral part of its success. Your goals and objectives will determine what your research ultimately looks like in terms of scope, depth, and direction. You will gain clarity in your research and in the minds of your readers if you establish clear aims and objectives, with your aim stating what you wish to achieve, and your objectives indicating how you will do that. Moreover, you will have a clearer direction for your research if you take the time to establish your research objective and aim. This will lead to fewer future issues, but also to a more thorough and cohesive research project.

Feel free to reach out to us here if you need assistance in writing your objectives or aim for your thesis or research. You can also click here to learn more about the services provided.

شارك هذا الموضوع:

You might also like.

Read more about the article Tips for Writing a Strong Introduction for Your Academic Paper

Tips for Writing a Strong Introduction for Your Academic Paper

Read more about the article How to Write your Research Hypotheses?

How to Write your Research Hypotheses?

Read more about the article Is Your Research Topic Good?

Is Your Research Topic Good?

This post has one comment.

' src=

I’ve been browsing online greater than three hours these days, but I never discovered any interesting article like yours. It抯 lovely value sufficient for me. In my view, if all website owners and bloggers made excellent content as you probably did, the web can be a lot more useful than ever before.

Leave a Reply Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Notify me of follow-up comments by email.

Notify me of new posts by email.

  • Postgraduate Services
  • Undergraduates Services
  • Publishing & Reseach Services​
  • Request quote
  • Hedef Academy

Follow by Email

  • Defining Research Objectives: How To  Write Them

Moradeke Owa

Almost all industries use research for growth and development. Research objectives are how researchers ensure that their study has direction and makes a significant contribution to growing an industry or niche.

Research objectives provide a clear and concise statement of what the researcher wants to find out. As a researcher, you need to clearly outline and define research objectives to guide the research process and ensure that the study is relevant and generates the impact you want.

In this article, we will explore research objectives and how to leverage them to achieve successful research studies.

What Are Research Objectives?

Research objectives are what you want to achieve through your research study. They guide your research process and help you focus on the most important aspects of your topic.

You can also define the scope of your study and set realistic and attainable study goals with research objectives. For example, with clear research objectives, your study focuses on the specific goals you want to achieve and prevents you from spending time and resources collecting unnecessary data.

However, sticking to research objectives isn’t always easy, especially in broad or unconventional research. This is why most researchers follow the SMART criteria when defining their research objectives.

Understanding SMART Criteria in Research

Think of research objectives as a roadmap to achieving your research goals, with the SMART criteria as your navigator on the map.

SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound. These criteria help you ensure that your research objectives are clear, specific, realistic, meaningful, and time-bound.

Here’s a breakdown of the SMART Criteria:

Specific : Your research objectives should be clear: what do you want to achieve, why do you want to achieve it, and how do you plan to achieve it? Avoid vague or broad statements that don’t provide enough direction for your research.

Measurable : Your research objectives should have metrics that help you track your progress and measure your results. Also, ensure the metrics are measurable with data to verify them.

Achievable : Your research objectives should be within your research scope, timeframe, and budget. Also, set goals that are challenging but not impossible.

Relevant: Your research objectives should be in line with the goal and significance of your study. Also, ensure that the objectives address a specific issue or knowledge gap that is interesting and relevant to your industry or niche.

Time-bound : Your research objectives should have a specific deadline or timeframe for completion. This will help you carefully set a schedule for your research activities and milestones and monitor your study progress.

Characteristics of Effective Research Objectives

Clarity : Your objectives should be clear and unambiguous so that anyone who reads them can understand what you intend to do. Avoid vague or general terms that could be taken out of context.

Specificity : Your objectives should be specific and address the research questions that you have formulated. Do not use broad or narrow objectives as they may restrict your field of research or make your research irrelevant.

Measurability : Define your metrics with indicators or metrics that help you determine if you’ve accomplished your goals or not. This will ensure you are tracking the research progress and making interventions when needed.

Also, do use objectives that are subjective or based on personal opinions, as they may be difficult to accurately verify and measure.

Achievability : Your objectives should be realistic and attainable, given the resources and time available for your research project. You should set objectives that match your skills and capabilities, they can be difficult but not so hard that they are realistically unachievable.

For example, setting very difficult make you lose confidence, and abandon your research. Also, setting very simple objectives could demotivate you and prevent you from closing the knowledge gap or making significant contributions to your field with your research.

Relevance : Your objectives should be relevant to your research topic and contribute to the existing knowledge in your field. Avoid objectives that are unrelated or insignificant, as they may waste your time or resources.

Time-bound : Your objectives should be time-bound and specify when you will complete them. Have a realistic and flexible timeframe for achieving your objectives, and track your progress with it. 

Steps to Writing Research Objectives

Identify the research questions.

The first step in writing effective research objectives is to identify the research questions that you are trying to answer. Research questions help you narrow down your topic and identify the gaps or problems that you want to address with your research.

For example, if you are interested in the impact of technology on children’s development, your research questions could be:

  • What is the relationship between technology use and academic performance among children?
  • Are children who use technology more likely to do better in school than those who do not?
  • What is the social and psychological impact of technology use on children?

Brainstorm Objectives

Once you have your research questions, you can brainstorm possible objectives that relate to them. Objectives are more specific than research questions, and they tell you what you want to achieve or learn in your research.

You can use verbs such as analyze, compare, evaluate, explore, investigate, etc. to express your objectives. Also, try to generate as many objectives as possible, without worrying about their quality or feasibility at this stage.

Prioritize Objectives

Once you’ve brainstormed your objectives, you’ll need to prioritize them based on their relevance and feasibility. Relevance is how relevant the objective is to your research topic and how well it fits into your overall research objective.

Feasibility is how realistic and feasible the objective is compared to the time, money, and expertise you have. You can create a matrix or ranking system to organize your objectives and pick the ones that matter the most.

Refine Objectives

The next step is to refine and revise your objectives to ensure clarity and specificity. Start by ensuring that your objectives are consistent and coherent with each other and with your research questions. 

Make Objectives SMART

A useful way to refine your objectives is to make them SMART, which stands for specific, measurable, achievable, relevant, and time-bound. 

  • Specific : Objectives should clearly state what you hope to achieve.
  • Measurable : They should be able to be quantified or evaluated.
  • Achievable : realistic and within the scope of the research study.
  • Relevant : They should be directly related to the research questions.
  • Time-bound : specific timeframe for research completion.

Review and Finalize Objectives

The final step is to review your objectives for coherence and alignment with your research questions and aim. Ensure your objectives are logically connected and consistent with each other and with the purpose of your study.

You also need to check that your objectives are not too broad or too narrow, too easy or too hard, too many or too few. You can use a checklist or a rubric to evaluate your objectives and make modifications.

Examples of Well-Written Research Objectives

Example 1- Psychology

Research question: What are the effects of social media use on teenagers’ mental health?

Objective : To determine the relationship between the amount of time teenagers in the US spend on social media and their levels of anxiety and depression before and after using social media.

What Makes the Research Objective SMART?

The research objective is specific because it clearly states what the researcher hopes to achieve. It is measurable because it can be quantified by measuring the levels of anxiety and depression in teenagers. 

Also, the objective is achievable because the researcher can collect enough data to answer the research question. It is relevant because it is directly related to the research question. It is time-bound because it has a specific deadline for completion.

Example 2- Marketing

Research question : How can a company increase its brand awareness by 10%?

Objective : To develop a marketing strategy that will increase the company’s sales by 10% within the next quarter.

How Is this Research Objective SMART?

The research states what the researcher hopes to achieve ( Specific ). You can also measure the company’s reach before and after the marketing plan is implemented ( Measurable ).

The research objective is also achievable because you can develop a marketing plan that will increase awareness by 10% within the timeframe. The objective is directly related to the research question ( Relevant ). It is also time-bound because it has a specific deadline for completion.

Research objectives are a well-designed roadmap to completing and achieving your overall research goal. 

However, research goals are only effective if they are well-defined and backed up with the best practices such as the SMART criteria. Properly defining research objectives will help you plan and conduct your research project effectively and efficiently.

Logo

Connect to Formplus, Get Started Now - It's Free!

  • research goals
  • research objectives
  • research roadmap
  • smart goals
  • SMART research objectives
  • Moradeke Owa

Formplus

You may also like:

Research Summary: What Is It & How To Write One

Introduction A research summary is a requirement during academic research and sometimes you might need to prepare a research summary...

what is research aims and objectives

Projective Techniques In Surveys: Definition, Types & Pros & Cons

Introduction When you’re conducting a survey, you need to find out what people think about things. But how do you get an accurate and...

Subgroup Analysis: What It Is + How to Conduct It

Introduction Clinical trials are an integral part of the drug development process. They aim to assess the safety and efficacy of a new...

Desk Research: Definition, Types, Application, Pros & Cons

If you are looking for a way to conduct a research study while optimizing your resources, desk research is a great option. Desk research...

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

404 Not found

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • v.53(4); 2010 Aug

Logo of canjsurg

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

  • Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
  • Learn about current trends and technological advances on the topic.
  • Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
  • Use the FINER criteria in the development of the research question.
  • Ensure that the research question follows PICOT format.
  • Develop a research hypothesis from the research question.
  • Develop clear and well-defined primary and secondary (if needed) objectives.
  • Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

Frequently asked questions

What’s the difference between research aims and objectives.

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Ask our team

Want to contact us directly? No problem. We are always here for you.

Support team - Nina

Our support team is here to help you daily via chat, WhatsApp, email, or phone between 9:00 a.m. to 11:00 p.m. CET.

Our APA experts default to APA 7 for editing and formatting. For the Citation Editing Service you are able to choose between APA 6 and 7.

Yes, if your document is longer than 20,000 words, you will get a sample of approximately 2,000 words. This sample edit gives you a first impression of the editor’s editing style and a chance to ask questions and give feedback.

How does the sample edit work?

You will receive the sample edit within 24 hours after placing your order. You then have 24 hours to let us know if you’re happy with the sample or if there’s something you would like the editor to do differently.

Read more about how the sample edit works

Yes, you can upload your document in sections.

We try our best to ensure that the same editor checks all the different sections of your document. When you upload a new file, our system recognizes you as a returning customer, and we immediately contact the editor who helped you before.

However, we cannot guarantee that the same editor will be available. Your chances are higher if

  • You send us your text as soon as possible and
  • You can be flexible about the deadline.

Please note that the shorter your deadline is, the lower the chance that your previous editor is not available.

If your previous editor isn’t available, then we will inform you immediately and look for another qualified editor. Fear not! Every Scribbr editor follows the  Scribbr Improvement Model  and will deliver high-quality work.

Yes, our editors also work during the weekends and holidays.

Because we have many editors available, we can check your document 24 hours per day and 7 days per week, all year round.

If you choose a 72 hour deadline and upload your document on a Thursday evening, you’ll have your thesis back by Sunday evening!

Yes! Our editors are all native speakers, and they have lots of experience editing texts written by ESL students. They will make sure your grammar is perfect and point out any sentences that are difficult to understand. They’ll also notice your most common mistakes, and give you personal feedback to improve your writing in English.

Every Scribbr order comes with our award-winning Proofreading & Editing service , which combines two important stages of the revision process.

For a more comprehensive edit, you can add a Structure Check or Clarity Check to your order. With these building blocks, you can customize the kind of feedback you receive.

You might be familiar with a different set of editing terms. To help you understand what you can expect at Scribbr, we created this table:

View an example

When you place an order, you can specify your field of study and we’ll match you with an editor who has familiarity with this area.

However, our editors are language specialists, not academic experts in your field. Your editor’s job is not to comment on the content of your dissertation, but to improve your language and help you express your ideas as clearly and fluently as possible.

This means that your editor will understand your text well enough to give feedback on its clarity, logic and structure, but not on the accuracy or originality of its content.

Good academic writing should be understandable to a non-expert reader, and we believe that academic editing is a discipline in itself. The research, ideas and arguments are all yours – we’re here to make sure they shine!

After your document has been edited, you will receive an email with a link to download the document.

The editor has made changes to your document using ‘Track Changes’ in Word. This means that you only have to accept or ignore the changes that are made in the text one by one.

It is also possible to accept all changes at once. However, we strongly advise you not to do so for the following reasons:

  • You can learn a lot by looking at the mistakes you made.
  • The editors don’t only change the text – they also place comments when sentences or sometimes even entire paragraphs are unclear. You should read through these comments and take into account your editor’s tips and suggestions.
  • With a final read-through, you can make sure you’re 100% happy with your text before you submit!

You choose the turnaround time when ordering. We can return your dissertation within 24 hours , 3 days or 1 week . These timescales include weekends and holidays. As soon as you’ve paid, the deadline is set, and we guarantee to meet it! We’ll notify you by text and email when your editor has completed the job.

Very large orders might not be possible to complete in 24 hours. On average, our editors can complete around 13,000 words in a day while maintaining our high quality standards. If your order is longer than this and urgent, contact us to discuss possibilities.

Always leave yourself enough time to check through the document and accept the changes before your submission deadline.

Scribbr is specialised in editing study related documents. We check:

  • Graduation projects
  • Dissertations
  • Admissions essays
  • College essays
  • Application essays
  • Personal statements
  • Process reports
  • Reflections
  • Internship reports
  • Academic papers
  • Research proposals
  • Prospectuses

Calculate the costs

The fastest turnaround time is 24 hours.

You can upload your document at any time and choose between three deadlines:

At Scribbr, we promise to make every customer 100% happy with the service we offer. Our philosophy: Your complaint is always justified – no denial, no doubts.

Our customer support team is here to find the solution that helps you the most, whether that’s a free new edit or a refund for the service.

Yes, in the order process you can indicate your preference for American, British, or Australian English .

If you don’t choose one, your editor will follow the style of English you currently use. If your editor has any questions about this, we will contact you.

Research Objectives: The Compass of Your Study

image

Table of contents

  • 1 Definition and Purpose of Setting Clear Research Objectives
  • 2 How Research Objectives Fit into the Overall Research Framework
  • 3 Types of Research Objectives
  • 4 Aligning Objectives with Research Questions and Hypotheses
  • 5 Role of Research Objectives in Various Research Phases
  • 6.1 Key characteristics of well-defined research objectives
  • 6.2 Step-by-Step Guide on How to Formulate Both General and Specific Research Objectives
  • 6.3 How to Know When Your Objectives Need Refinement
  • 7 Research Objectives Examples in Different Fields
  • 8 Conclusion

Embarking on a research journey without clear objectives is like navigating the sea without a compass. This article delves into the essence of establishing precise research objectives, serving as the guiding star for your scholarly exploration.

We will unfold the layers of how the objective of study not only defines the scope of your research but also directs every phase of the research process, from formulating research questions to interpreting research findings. By bridging theory with practical examples, we aim to illuminate the path to crafting effective research objectives that are both ambitious and attainable. Let’s chart the course to a successful research voyage, exploring the significance, types, and formulation of research paper objectives.

Definition and Purpose of Setting Clear Research Objectives

Defining the research objectives includes which two tasks? Research objectives are clear and concise statements that outline what you aim to achieve through your study. They are the foundation for determining your research scope, guiding your data collection methods, and shaping your analysis. The purpose of research proposal and setting clear objectives in it is to ensure that your research efforts are focused and efficient, and to provide a roadmap that keeps your study aligned with its intended outcomes.

To define the research objective at the outset, researchers can avoid the pitfalls of scope creep, where the study’s focus gradually broadens beyond its initial boundaries, leading to wasted resources and time. Clear objectives facilitate communication with stakeholders, such as funding bodies, academic supervisors, and the broader academic community, by succinctly conveying the study’s goals and significance. Furthermore, they help in the formulation of precise research questions and hypotheses, making the research process more systematic and organized. Yet, it is not always easy. For this reason, PapersOwl is always ready to help. Lastly, clear research objectives enable the researcher to critically assess the study’s progress and outcomes against predefined benchmarks, ensuring the research stays on track and delivers meaningful results.

How Research Objectives Fit into the Overall Research Framework

Research objectives are integral to the research framework as the nexus between the research problem, questions, and hypotheses. They translate the broad goals of your study into actionable steps, ensuring every aspect of your research is purposefully aligned towards addressing the research problem. This alignment helps in structuring the research design and methodology, ensuring that each component of the study is geared towards answering the core questions derived from the objectives. Creating such a difficult piece may take a lot of time. If you need it to be accurate yet fast delivered, consider getting professional research paper writing help whenever the time comes. It also aids in the identification and justification of the research methods and tools used for data collection and analysis, aligning them with the objectives to enhance the validity and reliability of the findings.

Furthermore, by setting clear objectives, researchers can more effectively evaluate the impact and significance of their work in contributing to existing knowledge. Additionally, research objectives guide literature review, enabling researchers to focus their examination on relevant studies and theoretical frameworks that directly inform their research goals.

Types of Research Objectives

In the landscape of research, setting objectives is akin to laying down the tracks for a train’s journey, guiding it towards its destination. Constructing these tracks involves defining two main types of objectives: general and specific. Each serves a unique purpose in guiding the research towards its ultimate goals, with general objectives providing the broad vision and specific objectives outlining the concrete steps needed to fulfill that vision. Together, they form a cohesive blueprint that directs the focus of the study, ensuring that every effort contributes meaningfully to the overarching research aims.

  • General objectives articulate the overarching goals of your study. They are broad, setting the direction for your research without delving into specifics. These objectives capture what you wish to explore or contribute to existing knowledge.
  • Specific objectives break down the general objectives into measurable outcomes. They are precise, detailing the steps needed to achieve the broader goals of your study. They often correspond to different aspects of your research question , ensuring a comprehensive approach to your study.

To illustrate, consider a research project on the impact of digital marketing on consumer behavior. A general objective might be “to explore the influence of digital marketing on consumer purchasing decisions.” Specific objectives could include “to assess the effectiveness of social media advertising in enhancing brand awareness” and “to evaluate the impact of email marketing on customer loyalty.”

Aligning Objectives with Research Questions and Hypotheses

The harmony between what research objectives should be, questions, and hypotheses is critical. Objectives define what you aim to achieve; research questions specify what you seek to understand, and hypotheses predict the expected outcomes.

This alignment ensures a coherent and focused research endeavor. Achieving it necessitates a thoughtful consideration of how each component interrelates, ensuring that the objectives are not only ambitious but also directly answerable through the research questions and testable via the hypotheses. This interconnectedness facilitates a streamlined approach to the research process, enabling researchers to systematically address each aspect of their study in a logical sequence. Moreover, it enhances the clarity and precision of the research, making it easier for peers and stakeholders to grasp the study’s direction and potential contributions.

Role of Research Objectives in Various Research Phases

Throughout the research process, objectives guide your choices and strategies – from selecting the appropriate research design and methods to analyzing data and interpreting results. They are the criteria against which you measure the success of your study. In the initial stages, research objectives inform the selection of a topic, helping to narrow down a broad area of interest into a focused question that can be explored in depth. During the methodology phase, they dictate the type of data needed and the best methods for obtaining that data, ensuring that every step taken is purposeful and aligned with the study’s goals. As the research progresses, objectives provide a framework for analyzing the collected data, guiding the researcher in identifying patterns, drawing conclusions, and making informed decisions.

Crafting Effective Research Objectives

pic

The effective objective of research is pivotal in laying the groundwork for a successful investigation. These objectives clarify the focus of your study and determine its direction and scope. Ensuring that your objectives are well-defined and aligned with the SMART criteria is crucial for setting a strong foundation for your research.

Key characteristics of well-defined research objectives

Well-defined research objectives are characterized by the SMART criteria – Specific, Measurable, Achievable, Relevant, and Time-bound. Specific objectives clearly define what you plan to achieve, eliminating any ambiguity. Measurable objectives allow you to track progress and assess the outcome. Achievable objectives are realistic, considering the research sources and time available. Relevant objectives align with the broader goals of your field or research question. Finally, Time-bound objectives have a clear timeline for completion, adding urgency and a schedule to your work.

Step-by-Step Guide on How to Formulate Both General and Specific Research Objectives

So lets get to the part, how to write research objectives properly?

  • Understand the issue or gap in existing knowledge your study aims to address.
  • Gain insights into how similar challenges have been approached to refine your objectives.
  • Articulate the broad goal of research based on your understanding of the problem.
  • Detail the specific aspects of your research, ensuring they are actionable and measurable.

How to Know When Your Objectives Need Refinement

Your objectives of research may require refinement if they lack clarity, feasibility, or alignment with the research problem. If you find yourself struggling to design experiments or methods that directly address your objectives, or if the objectives seem too broad or not directly related to your research question, it’s likely time for refinement. Additionally, objectives in research proposal that do not facilitate a clear measurement of success indicate a need for a more precise definition. Refinement involves ensuring that each objective is specific, measurable, achievable, relevant, and time-bound, enhancing your research’s overall focus and impact.

Research Objectives Examples in Different Fields

The application of research objectives spans various academic disciplines, each with its unique focus and methodologies. To illustrate how the objectives of the study guide a research paper across different fields, here are some research objective examples:

  • In Health Sciences , a research aim may be to “determine the efficacy of a new vaccine in reducing the incidence of a specific disease among a target population within one year.” This objective is specific (efficacy of a new vaccine), measurable (reduction in disease incidence), achievable (with the right study design and sample size), relevant (to public health), and time-bound (within one year).
  • In Environmental Studies , the study objectives could be “to assess the impact of air pollution on urban biodiversity over a decade.” This reflects a commitment to understanding the long-term effects of human activities on urban ecosystems, emphasizing the need for sustainable urban planning.
  • In Economics , an example objective of a study might be “to analyze the relationship between fiscal policies and unemployment rates in developing countries over the past twenty years.” This seeks to explore macroeconomic trends and inform policymaking, highlighting the role of economic research study in societal development.

These examples of research objectives describe the versatility and significance of research objectives in guiding scholarly inquiry across different domains. By setting clear, well-defined objectives, researchers can ensure their studies are focused and impactful and contribute valuable knowledge to their respective fields.

Defining research studies objectives and problem statement is not just a preliminary step, but a continuous guiding force throughout the research journey. These goals of research illuminate the path forward and ensure that every stride taken is meaningful and aligned with the ultimate goals of the inquiry. Whether through the meticulous application of the SMART criteria or the strategic alignment with research questions and hypotheses, the rigor in crafting and refining these objectives underscores the integrity and relevance of the research. As scholars venture into the vast terrains of knowledge, the clarity, and precision of their objectives serve as beacons of light, steering their explorations toward discoveries that advance academic discourse and resonate with the broader societal needs.

Readers also enjoyed

Research Design Basics: Building Blocks of Scholarly Research

WHY WAIT? PLACE AN ORDER RIGHT NOW!

Just fill out the form, press the button, and have no worries!

We use cookies to give you the best experience possible. By continuing we’ll assume you board with our cookie policy.

what is research aims and objectives

  • Research article
  • Open access
  • Published: 15 April 2024

What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

  • Trisha Greenhalgh   ORCID: orcid.org/0000-0003-2369-8088 1 ,
  • Julie L. Darbyshire 1 ,
  • Cassie Lee 2 ,
  • Emma Ladds 1 &
  • Jenny Ceolta-Smith 3  

BMC Medicine volume  22 , Article number:  159 ( 2024 ) Cite this article

1512 Accesses

66 Altmetric

Metrics details

Long covid (post covid-19 condition) is a complex condition with diverse manifestations, uncertain prognosis and wide variation in current approaches to management. There have been calls for formal quality standards to reduce a so-called “postcode lottery” of care. The original aim of this study—to examine the nature of quality in long covid care and reduce unwarranted variation in services—evolved to focus on examining the reasons why standardizing care was so challenging in this condition.

In 2021–2023, we ran a quality improvement collaborative across 10 UK sites. The dataset reported here was mostly but not entirely qualitative. It included data on the origins and current context of each clinic, interviews with staff and patients, and ethnographic observations at 13 clinics (50 consultations) and 45 multidisciplinary team (MDT) meetings (244 patient cases). Data collection and analysis were informed by relevant lenses from clinical care (e.g. evidence-based guidelines), improvement science (e.g. quality improvement cycles) and philosophy of knowledge.

Participating clinics made progress towards standardizing assessment and management in some topics; some variation remained but this could usually be explained. Clinics had different histories and path dependencies, occupied a different place in their healthcare ecosystem and served a varied caseload including a high proportion of patients with comorbidities. A key mechanism for achieving high-quality long covid care was when local MDTs deliberated on unusual, complex or challenging cases for which evidence-based guidelines provided no easy answers. In such cases, collective learning occurred through idiographic (case-based) reasoning , in which practitioners build lessons from the particular to the general. This contrasts with the nomothetic reasoning implicit in evidence-based guidelines, in which reasoning is assumed to go from the general (e.g. findings of clinical trials) to the particular (management of individual patients).

Not all variation in long covid services is unwarranted. Largely because long covid’s manifestations are so varied and comorbidities common, generic “evidence-based” standards require much individual adaptation. In this complex condition, quality improvement resources may be productively spent supporting MDTs to optimise their case-based learning through interdisciplinary discussion. Quality assessment of a long covid service should include review of a sample of individual cases to assess how guidelines have been interpreted and personalized to meet patients’ unique needs.

Study registration

NCT05057260, ISRCTN15022307.

Peer Review reports

The term “long covid” [ 1 ] means prolonged symptoms following SARS-CoV-2 infection not explained by an alternative diagnosis [ 2 ]. It embraces the US term “post-covid conditions” (symptoms beyond 4 weeks) [ 3 ], the UK terms “ongoing symptomatic covid-19” (symptoms lasting 4–12 weeks) and “post covid-19 syndrome” (symptoms beyond 12 weeks) [ 4 ] and the World Health Organization’s “post covid-19 condition” (symptoms occurring beyond 3 months and persisting for at least 2 months) [ 5 ]. Long covid thus defined is extremely common. In UK, for example, 1.8 million of a population of 67 million met the criteria for long covid in early 2023 and 41% of these had been unwell for more than 2 years [ 6 ].

Long covid is characterized by a constellation of symptoms which may include breathlessness, fatigue, muscle and joint pain, chest pain, memory loss and impaired concentration (“brain fog”), sleep disturbance, depression, anxiety, palpitations, dizziness, gastrointestinal problems such as diarrhea, skin rashes and allergy to food or drugs [ 2 ]. These lead to difficulties with essential daily activities such as washing and dressing, impaired exercise tolerance and ability to work, and reduced quality of life [ 2 , 7 , 8 ]. Symptoms typically cluster (e.g. in different patients, long covid may be dominated by fatigue, by breathlessness or by palpitations and dizziness) [ 9 , 10 ]. Long covid may follow a fairly constant course or a relapsing and remitting one, perhaps with specific triggers [ 11 ]. Overlaps between fatigue-dominant subtypes of long covid, myalgic encephalomyelitis and chronic fatigue syndrome have been hypothesized [ 12 ] but at the time of writing remain unproven.

Long covid has been a contested condition from the outset. Whilst long-term sequelae following other coronavirus (SARS and MERS) infections were already well-documented [ 13 ], SARS-CoV-2 was originally thought to cause a short-lived respiratory illness from which the patient either died or recovered [ 14 ]. Some clinicians dismissed protracted or relapsing symptoms as due to anxiety or deconditioning, especially if the patient had not had laboratory-confirmed covid-19. People with long covid got together in online groups and shared accounts of their symptoms and experiences of such “gaslighting” in their healthcare encounters [ 15 , 16 ]. Some groups conducted surveys on their members, documenting the wide range of symptoms listed in the previous paragraph and showing that whilst long covid is more commonly a sequel to severe acute covid-19, it can (rarely) follow a mild or even asymptomatic acute infection [ 17 ].

Early publications on long covid depicted a post-pneumonia syndrome which primarily affected patients who had been hospitalized (and sometimes ventilated) [ 18 , 19 ]. Later, covid-19 was recognized to be a multi-organ inflammatory condition (the pneumonia, for example, was reclassified as pneumonitis ) and its long-term sequelae attributed to a combination of viral persistence, dysregulated immune response (including auto-immunity), endothelial dysfunction and immuno-thrombosis, leading to damage to the lining of small blood vessels and (thence) interference with transfer of oxygen and nutrients to vital organs [ 20 , 21 , 22 , 23 , 24 ]. But most such studies were highly specialized, laboratory-based and written primarily for an audience of fellow laboratory researchers. Despite demonstrating mean differences in a number of metabolic variables, they failed to identify a reliable biomarker that could be used routinely in the clinic to rule a diagnosis of long covid in or out. Whilst the evidence base from laboratory studies grew rapidly, it had little influence on clinical management—partly because most long covid clinics had been set up with impressive speed by front-line clinical teams to address an immediate crisis, with little or no input from immunologists, virologists or metabolic specialists [ 25 ].

Studies of the patient experience revealed wide geographical variation in whether any long covid services were provided and (if they were) which patients were eligible for these and what tests and treatments were available [ 26 ]. An interim UK clinical guideline for long covid had been produced at speed and published in December 2020 [ 27 ], but it was uncertain about diagnostic criteria, investigations, treatments and prognosis. Early policy recommendations for long covid services in England, based on wide consultation across UK, had proposed a tiered service with “tier 1” being supported self-management, “tier 2” generalist assessment and management in primary care, “tier 3” specialist rehabilitation or respiratory follow-up with oversight from a consultant physician and “tier 4” tertiary care for patients with complications or complex needs [ 28 ]. In 2021, ring-fenced funding was allocated to establish 90 multidisciplinary long covid clinics in England [ 29 ]; some clinics were also set up with local funding in Scotland and Wales. These clinics varied widely in eligibility criteria, referral pathways, staffing mix (some had no doctors at all) and investigations and treatments offered. A further policy document on improving long covid services was published in 2022 [ 30 ]; it recommended that specialist long covid clinics should continue, though the long-term funding of these services remains uncertain [ 31 ]. To build the evidence base for delivering long covid services, major programs of publicly funded research were commenced in both UK [ 32 ] and USA [ 33 ].

In short, at the time this study began (late 2021), there appeared to be much scope for a program of quality improvement which would capture fast-emerging research findings, establish evidence-based standards and ensure these were rapidly disseminated and consistently adopted across both specialist long covid services and in primary care.

Quality improvement collaboratives

The quality improvement movement in healthcare was born in the early 1980s when clinicians and policymakers US and UK [ 34 , 35 , 36 , 37 ] began to draw on insights from outside the sector [ 38 , 39 , 40 ]. Adapting a total quality management approach that had previously transformed the Japanese car industry, they sought to improve efficiency, reduce waste, shift to treating the upstream causes of problems (hence preventing disease) and help all services approach the standards of excellence achieved by the best. They developed an approach based on (a) understanding healthcare as a complex system (especially its key interdependencies and workflows), (b) analysing and addressing variation within the system, (c) learning continuously from real-world data and (d) developing leaders who could motivate people and help them change structures and processes [ 41 , 42 , 43 , 44 ].

Quality improvement collaboratives (originally termed “breakthrough collaboratives” [ 45 ]), in which representatives from different healthcare organizations come together to address a common problem, identify best practice, set goals, share data and initiate and evaluate improvement efforts [ 46 ], are one model used to deliver system-wide quality improvement. It is widely assumed that these collaboratives work because—and to the extent that—they identify, interpret and implement high-quality evidence (e.g. from randomized controlled trials).

Research on why quality improvement collaboratives succeed or fail has produced the following list of critical success factors: taking a whole-system approach, selecting a topic and goal that fits with organizations’ priorities, fostering a culture of quality improvement (e.g. that quality is everyone’s job), engagement of everyone (including the multidisciplinary clinical team, managers, patients and families) in the improvement effort, clearly defining people’s roles and contribution, engaging people in preliminary groundwork, providing organizational-level support (e.g. chief executive endorsement, protected staff time, training and support for teams, resources, quality-focused human resource practices, external facilitation if needed), training in specific quality improvement techniques (e.g. plan-do-study-act cycle), attending to the human dimension (including cultivating trust and working to ensure shared vision and buy-in), continuously generating reliable data on both processes (e.g. current practice) and outcomes (clinical, satisfaction) and a “learning system” infrastructure in which knowledge that is generated feeds into individual, team and organizational learning [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ].

The quality improvement collaborative approach has delivered many successes but it has been criticized at a theoretical level for over-simplifying the social science of human motivation and behaviour and for adopting a somewhat mechanical approach to the study of complex systems [ 55 , 56 ]. Adaptations of the original quality improvement methodology (e.g. from Sweden [ 57 , 58 ]) have placed greater emphasis on human values and meaning-making, on the grounds that reducing the complexities of a system-wide quality improvement effort to a set of abstract and generic “success factors” will miss unique aspects of the case such as historical path dependencies, personalities, framing and meaning-making and micropolitics [ 59 ].

Perhaps this explains why, when the abovementioned factors are met, a quality improvement collaborative’s success is more likely but is not guaranteed, as a systematic review demonstrated [ 60 ]. Some well-designed and well-resourced collaboratives addressing clear knowledge gaps produced few or no sustained changes in key outcome measures [ 49 , 53 , 60 , 61 , 62 ]. To identify why this might be, a detailed understanding of a service’s history, current challenges and contextual constraints is needed. This explains our decision, part-way through the study reported here, to collect rich contextual data on participating sites so as to better explain success or failure of our own collaborative.

Warranted and unwarranted variation in clinical practice

A generation ago, Wennberg described most variation in clinical practice as “unwarranted” (which he defined as variation in the utilization of health care services that cannot be explained by variation in patient illness or patient preferences) [ 63 ]. Others coined the term “postcode lottery” to depict how such variation allegedly impacted on health outcomes [ 64 ]. Wennberg and colleagues’ Atlas of Variation , introduced in 1999 [ 65 ], and its UK equivalent, introduced in 2010 [ 66 ], described wide regional differences in the rates of procedures from arthroscopy to hysterectomy, and were used to prompt services to identify and address examples of under-treatment, mis-treatment and over-treatment. Numerous similar initiatives, mostly based on hospital activity statistics, have been introduced around the world [ 66 , 67 , 68 , 69 ]. Sutherland and Levesque’s proposed framework for analysing variation, for example, has three domains: capacity (broadly, whether sufficient resources are allocated at organizational level and whether individuals have the time and headspace to get involved), evidence (the extent to which evidence-based guidelines exist and are followed), and agency (e.g. whether clinicians are engaged with the issue and the effect of patient choice) [ 70 ].

Whilst it is clearly a good idea to identify unwarranted variation in practice, it is also important to acknowledge that variation can be warranted . The very act of measuring and describing variation carries great rhetorical power, since revealing geographical variation in any chosen metric effectively frames this as a problem with a conceptually simple solution (reducing variation) that will appeal to both politicians and the public [ 71 ]. The temptation to expose variation (e.g. via visualizations such as maps) and address it in mechanistic ways should be resisted until we have fully understood the reasons why it exists, which may include perverse incentives, insufficient opportunities to discuss cases with colleagues, weak or absent feedback on practice, unclear decision processes, contested definitions of appropriate care and professional challenges to guidelines [ 72 ].

Research question, aims and objectives

Research question.

What is quality in long covid care and how can it best be achieved?

To identify best practice and reduce unwarranted variation in UK long covid services.

To explain aspects of variation in long covid services that are or may be warranted.

Our original objectives were to:

Establish a quality improvement collaborative for 10 long covid clinics across UK.

Use quality improvement methods in collaboration with patients and clinic staff to prioritize aspects of care to improve. For each priority topic, identify best (evidence-informed) clinical practice, measure performance in each clinic, compare performance with a best practice benchmark and improve performance.

Produce organizational case studies of participating long covid clinics to explain their origins, evolution, leadership, ethos, population served, patient pathways and place in the wider healthcare ecosystem.

Examine these case studies to explain variation in practice, especially in topics where the quality improvement cycle proves difficult to follow or has limited impact.

The LOCOMOTION study

LOCOMOTION (LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS) was a 30-month multi-site case study of 10 long covid clinics (8 in England, 1 in Wales and 1 in Scotland), beginning in 2021, which sought to optimise long covid care. Each clinic offered multidisciplinary care to patients referred from primary or secondary care (and, in some cases, self-referred), and held regular multidisciplinary team (MDT) meetings, mostly online via Microsoft Teams, to discuss cases. A study protocol for LOCOMOTION, with details of ethical approvals, management, governance and patient involvement has been published [ 25 ]. The three main work packages addressed quality improvement, technology-supported patient self-management and phenotyping and symptom clustering. This paper reports on the first work package, focusing mainly on qualitative findings.

Setting up the quality improvement collaborative

We broadly followed standard methodology for “breakthrough” quality improvement collaboratives [ 44 , 45 ], with two exceptions. First, because of geographical distance, continuing pandemic precautions and developments in videoconferencing technology, meetings were held online. Second, unlike in the original breakthrough model, patients were included in the collaborative, reflecting the cultural change towards patient partnerships since the model was originally proposed 40 years ago.

Each site appointed a clinical research fellow (doctor, nurse or allied health professional) funded partly by the LOCOMOTION study and partly with clinical sessions; some were existing staff who were backfilled to take on a research role whilst others were new appointments. The quality improvement meetings were held approximately every 8 weeks on Microsoft Teams and lasted about 2 h; there was an agenda and a chair, and meetings were recorded with consent. The clinical research fellow from each clinic attended, sometimes joined by the clinical lead for that site. In the initial meeting, the group proposed and prioritized topics before merging their consensus with the list of priority topics generated separately by patients (there was much overlap but also some differences).

In subsequent meetings, participants attempted to reach consensus on how to define, measure and achieve quality for each priority topic in turn, implement this approach in their own clinic and monitor its impact. Clinical leads prepared illustrative clinical cases and summaries of the research evidence, which they presented using Microsoft Powerpoint; the group then worked towards consensus on the implications for practice through general discussion. Clinical research fellows assisted with literature searches, collected baseline data from their own clinic, prepared and presented anonymized case examples, and contributed to collaborative goal-setting for improvement. Progress on each topic was reviewed at a later meeting after an agreed interval.

An additional element of this work package was semi-structured interviews with 29 patients, recruited from 9 of the 10 participating sites, about their clinic experiences with a view to feeding into service improvement (in the other site, no patient volunteered).

Our patient advisory group initially met separately from the quality improvement collaborative. They designed a short survey of current practice and sent it to each clinic; the results of this informed a prioritization exercise for topics where they considered change was needed. The patient-generated list was tabled at the quality improvement collaborative discussions, but patients were understandably keen to join these discussions directly. After about 9 months, some patient advisory group members joined the regular collaborative meetings. This dynamic was not without its tensions, since sharing performance data requires trust and there were some concerns about confidentiality when real patient cases were discussed with other patients present.

How evidence-informed quality targets were set

At the time the study began, there were no published large-scale randomized controlled trials of any interventions for long covid. We therefore followed a model used successfully in other quality improvement efforts where research evidence was limited or absent or it did not translate unambiguously into models for current services. In such circumstances, the best evidence may be custom and practice in the best-performing units. The quality improvement effort becomes oriented to what one group of researchers called “potentially better practices”—that is, practices that are “developed through analysis of the processes of care, literature review, and site visits” (page 14) [ 73 ]. The idea was that facilitated discussion among clinical teams, drawing on published research where available but also incorporating clinical experience, established practice and systematic analysis of performance data across participating clinics would surface these “potentially better practices”—an approach which, though not formally tested in controlled trials, appears to be associated with improved outcomes [ 46 , 73 ].

Adding an ethnographic component

Following limited progress made on some topics that had been designated high priority, we interviewed all 10 clinical research fellows (either individually or, in two cases, with a senior clinician present) and 18 other clinic staff (five individually plus two groups of 5 and 8), along with additional informal discussions, to explore the challenges of implementing the changes that had been agreed. These interviews were not audiotaped but detailed notes were made and typed up immediately afterwards. It became evident that some aspects of what the collaborative had deemed “evidence-informed” care were contested by front-line clinic staff, perceived as irrelevant to the service they were delivering, or considered impossible to implement. To unpack these issues further, the research protocol was amended to include an ethnographic component.

TG and EL (academic general practitioners) and JLD (a qualitative researcher with a PhD in the patient experience) attended a total of 45 MDT meetings in participating clinics (mostly online or hybrid). Staff were informed in advance that there would be an observer present; nobody objected. We noted brief demographic and clinical details of cases discussed (but no identifying data), dilemmas and uncertainties on which discussions focused, and how different staff members contributed.

TG made 13 in-person visits to participating long covid clinics. Staff were notified in advance; all were happy to be observed. Visits lasted between 5 and 8 h (54 h in total). We observed support staff booking patients in and processing requests and referrals, and shadowed different clinical staff in turn as they saw patients. Patients were informed of our presence and its purpose beforehand and given the opportunity to decline (three of 53 patients approached did). We discussed aspects of each case with the clinician after the patient left. When invited, we took breaks with staff and used these as an opportunity to ask them informally what it was like working in the clinic.

Ethnographic observation, analysis and reporting was geared to generating a rich interpretive account of the clinical, operational and interpersonal features of each clinic—what Van Maanen calls an “impressionist tales” [ 74 ]. Our work was also guided by the principles set out by Golden-Biddle and Locke, namely authenticity (spending time in the field and basing interpretations on these direct observations), plausibility (creating a plausible account through rich persuasive description) and criticality (e.g. reflexively examining our own assumptions) [ 75 ]. Our collection and analysis of qualitative data was informed by our own professional backgrounds (two general practitioners, one physical therapist, two non-clinicians).

In both MDTs and clinics, we took contemporaneous notes by hand and typed these up immediately afterwards.

Data management and analysis

Typed interview notes and field notes from clinics were collated in a set of Word documents, one for each clinic attended. They were analysed thematically [ 76 ] with attention to the literature on quality improvement and variation (see “ Background ”). Interim summaries were prepared on each clinic, setting out the narrative of how it had been established, its ethos and leadership, setting and staffing, population served and key links with other parts of the local healthcare ecosystem.

Minutes and field notes from the quality improvement collaborative meetings were summarized topic by topic, including initial data collected by the researchers-in-residence, improvement actions taken (or attempted) in that clinic, and any follow-up data shared. Progress or lack of it was interpreted in relation to the contextual case summary for that clinic.

Patient cases seen in clinic, and those discussed by MDTs, were summarized as brief case narratives in Word documents. Using the constant comparative method [ 77 ], we produced an initial synthesis of the clinical picture and principles of management based on the first 10 patient cases seen, and refined this as each additional case was added. Demographic and brief clinical and social details were also logged on Excel spreadsheets. When writing up clinical cases, we used the technique of composite case construction (in which we drew on several actual cases to generate a fictitious one, thereby protecting anonymity whilst preserving key empirical findings [ 78 ]); any names reported in this paper are pseudonyms.

Member checking

A summary was prepared for each clinic, including a narrative of the clinic’s own history and a summary of key quality issues raised across the ten clinics. These summaries included examples from real cases in our dataset. These were shared with the clinical research fellow and a senior clinician from the clinic, and amended in response to feedback. We also shared these summaries with representatives from the patient advisory group.

Overview of dataset

This study generated three complementary datasets. First, the video recordings, minutes, and field notes of 12 quality improvement collaborative meetings, along with the evidence summaries prepared for these meetings and clinic summaries (e.g. descriptions of current practice, audits) submitted by the clinical research fellows. This dataset illustrated wide variation in practice, and (in many topics) gaps or ambiguities in the evidence base.

Second, interviews with staff ( n  = 30) and patients ( n  = 29) from the clinics, along with ethnographic field notes (approximately 100 pages) from 13 in-person clinic visits (54 h), including notes on 50 patient consultations (40 face-to-face, 6 telephone, 4 video). This dataset illustrated the heterogeneity among the ten participating clinics.

Third, field notes (approximately 100 pages), including discussions on 244 clinical cases from the 45 MDT meetings (49 h) that we observed. This dataset revealed further similarities and contrasts among clinics in how patients were managed. In particular, it illustrated how, for the complex patients whose cases were presented at these meetings, teams made sense of, and planned for, each case through multidisciplinary dialogue. This dialogue typically began with one staff member presenting a detailed clinical history along with a narrative of how it had affected the patient’s life and what was at stake for them (e.g. job loss), after which professionals from various backgrounds (nursing, physical therapy, occupational therapy, psychology, dietetics, and different medical specialties) joined in a discussion about what to do.

The ten participating sites are summarized in Table  1 .

In the next two sections, we explore two issues—difficulty defining best practice and the heterogeneous nature of the clinics—that were key to explaining why quality, when pursued in a 10-site collaborative, proved elusive. We then briefly summarize patients’ accounts of their experience in the clinics and give three illustrative examples of the elusiveness of quality improvement using selected topics that were prioritized in our collaborative: outcome measures, investigation of palpitations and management of fatigue. In the final section of the results, we describe how MDT deliberations proved crucial for local quality improvement. Further detail on clinical priority topics will be presented in a separate paper.

“Best practice” in long covid: uncertainty and conflict

The study period (September 2021 to December 2023) corresponded with an exponential increase in published research on long covid. Despite this, the quality improvement collaborative found few unambiguous recommendations for practice. This gap between what the research literature offered and what clinical practice needed was partly ontological (relating what long covid is ). One major bone of contention between patients and clinicians (also evident in discussions with our patient advisory group), for example, was how far (and in whom) clinicians should look for and attempt to treat the various metabolic abnormalities that had been documented in laboratory research studies. The literature on this topic was extensive but conflicting [ 20 , 21 , 22 , 23 , 24 , 79 , 80 , 81 , 82 ]; it was heavy on biological detail but light on clinical application.

Patients were often aware of particular studies that appeared to offer plausible molecular or cellular explanations for symptom clusters along with a drug (often repurposed and off-label) whose mechanism of action appeared to be a good fit with the metabolic chain of causation. In one clinic, for example, we were shown an email exchange between a patient (not medically qualified) and a consultant, in which the patient asked them to reconsider their decision not to prescribe low-dose naltrexone, an opioid receptor antagonist with anti-inflammatory properties. The request included a copy of a peer-reviewed academic paper describing a small, uncontrolled pre-post study (i.e. a weak study design) in which this drug appeared to improve symptoms and functional performance in patients with long covid, as well as a mechanistic argument explaining why the patient felt this drug was a plausible choice in their own case.

This patient’s clinician, in common with most clinicians delivering front-line long covid services, considered that the evidence for such mechanism-based therapies was weak. Clinicians generally felt that this evidence, whilst promising, did not yet support routine measurement of clotting factors, antibodies, immune cells or other biomarkers or the prescription of mechanism-based therapies such as antivirals, anti-inflammatories or anticoagulants. Low-dose naltroxone, for example, is currently being tested in at least one randomized controlled trial (see National Clinical Trials Registry NCT05430152), which had not reported at the time of our observations.

Another challenge to defining best practice was the oft-repeated phrase that long covid is a “diagnosis by exclusion”, but the high prevalence of comorbidities meant that the “pure” long covid patient untainted by other potential explanations for their symptoms was a textbook ideal. In one MDT, for example, we observed a discussion about a patient who had had both swab-positive covid-19 and erythema migrans (a sign of Lyme disease) in the weeks before developing fatigue, yet local diagnostic criteria for each condition required the other to be excluded.

The logic of management in most participating clinics was pragmatic: prompt multidisciplinary assessment and treatment with an emphasis on obtaining a detailed clinical history (including premorbid health status), excluding serious complications (“red flags”), managing specific symptom clusters (for example, physical therapy for breathing pattern disorder), treating comorbidities (for example, anaemia, diabetes or menopause) and supporting whole-person rehabilitation [ 7 , 83 ]. The evidentiary questions raised in MDT discussions (which did not include patients) addressed the practicalities of the rehabilitation model (for example, whether cognitive therapy for neurocognitive complications is as effective when delivered online as it is when delivered in-person) rather than the molecular or cellular mechanisms of disease. For example, the question of whether patients with neurocognitive impairment should be tested for micro-clots or treated with anticoagulants never came up in the MDTs we observed, though we did visit a tertiary referral clinic (the tier 4 clinic in site H), whose lead clinician had a research interest in inflammatory coagulopathies and offered such tests to selected patients.

Because long covid typically produces dozens of symptoms that tend to be uniquely patterned in each patient, the uncertainties on which MDT discussions turned were rarely about general evidence of the kind that might be found in a guideline (e.g. how should fatigue be managed?). Rather they concerned particular case-based clinical decisions (e.g. how should this patient’s fatigue be managed, given the specifics of this case?). An example from our field notes illustrates this:

Physical therapist presents the case of a 39-year-old woman who works as a cleaner on an overnight ferry. Has had long covid for 2 years. Main symptoms are shortness of breath and possible anxiety attacks, especially when at work. She has had a course of physical therapy to teach diaphragmatic breathing but has found that focusing on her breathing makes her more anxious. Patient has to do a lot of bending in her job (e.g. cleaning toilets and under seats), which makes her dizzy, but Active Stand Test was normal. She also has very mild tricuspid incompetence [someone reads out a cardiology report—not hemodynamically significant].
Rehabilitation guidelines (e.g. WHO) recommend phased return to work (e.g. with reduced hours) and frequent breaks. “Tricky!” says someone. The job is intense and busy, and the patient can’t afford not to work. Discussion on whether all her symptoms can be attributed to tension and anxiety. Physical therapist who runs the breathing group says, “No, it’s long covid”, and describes severe initial covid-19 episode and results of serial chest X-rays which showed gradual clearing of ground glass shadows. Team discussion centers on how to negotiate reduced working hours in this particular job, given the overnight ferry shifts. --MDT discussion, Site D

This example raises important considerations about the nature of clinical knowledge in long covid. We return to it in the final section of the “ Results ” and in the “ Discussion ”.

Long covid clinics: a heterogeneous context for quality improvement

Most participating clinics had been established in mid-2020 to follow up patients who had been hospitalized (and perhaps ventilated) for severe acute covid-19. As mass vaccination reduced the severity of acute covid-19 for most people, the patient population in all clinics progressively shifted to include fewer “post-ICU [intensive care unit]” patients (in whom respiratory symptoms almost always dominated), and more people referred by their general practitioners or other secondary care specialties who had not been hospitalized for their acute covid-19 infection, and in whom fatigue, brain fog and palpitations were often the most troubling symptoms. Despite these similarities, the ten clinics had very different histories, geographical and material settings, staffing structures, patient pathways and case mix, as Table  1 illustrates. Below, we give more detail on three example sites.

Site C was established as a generalist “assessment-only” service by a general practitioner with an interest in infectious diseases. It is led jointly by that general practitioner and an occupational therapist, assisted by a wide range of other professionals including speech and language therapy, dietetics, clinical psychology and community-based physical therapy and occupational therapy. It has close links with a chronic fatigue service and a pain clinic that have been running in the locality for over 20 years. The clinic, which is entirely virtual (staff consult either from home or from a small side office in the community trust building), is physically located in a low-rise building on the industrial outskirts of a large town, sharing office space with various community-based health and social care services. Following a 1-h telephone consultation by one of the clinical leads, each patient is discussed at the MDT and then either discharged back to their general practitioner with a detailed management plan or referred on to one of the specialist services. This arrangement evolved to address a particular problem in this locality—that many patients with long covid were being referred by their general practitioner to multiple specialties (e.g. respiratory, neurology, fatigue), leading to a fragmented patient experience, unnecessary specialist assessments and wasteful duplication. The generalist assessment by telephone is oriented to documenting what is often a complex illness narrative (including pre-existing physical and mental comorbidities) and working with the patient to prioritize which symptoms or problems to pursue in which order.

Site E, in a well-regarded inner-city teaching hospital, had been set up in 2020 by a respiratory physician. Its initial ethos and rationale had been “respiratory follow-up”, with strong emphasis on monitoring lung damage via repeated imaging and lung function tests and in ensuring that patients received specialist physical therapy to “re-learn” efficient breathing techniques. Over time, this site has tried to accommodate a more multi-system assessment, with the introduction of a consultant-led infectious disease clinic for patients without a dominant respiratory component, reflecting the shift towards a more fatigue-predominant case mix. At the time of our fieldwork, each patient was seen in turn by a physician, psychologist, occupational therapist and respiratory physical therapist (half an hour each) before all four staff reconvened in a face-to-face MDT meeting to form a plan for each patient. But whilst a wide range of patients with diverse symptoms were discussed at these meetings, there remained a strong focus on respiratory pathology (e.g. tracking improvements in lung function and ensuring that coexisting asthma was optimally controlled).

Site F, one of the first long covid clinics in UK, was set up by a rehabilitation consultant who had been drafted to work on the ICU during the first wave of covid-19 in early 2020. He had a longstanding research interest in whole-patient rehabilitation, especially the assessment and management of chronic fatigue and pain. From the outset, clinic F was more oriented to rehabilitation, including vocational rehabilitation to help patients return to work. There was less emphasis on monitoring lung function or pursuing respiratory comorbidities. At the time of our fieldwork, clinic F offered both a community-based service (“tier 2”) led by an occupational therapist, supported by a respiratory physical therapist and psychologist, and a hospital-based service (“tier 3”) led by the rehabilitation consultant, supported by a wider MDT. Staff in both tiers emphasized that each patient needs a full physical and mental assessment and help to set and work towards achievable goals, whilst staying within safe limits so as to avoid post-exertional symptom exacerbation. Because of the research interest of the lead physician, clinic F adapted well to the growing numbers of patients with fatigue and quickly set up research studies on this cohort [ 84 ].

Details of the other seven sites are shown in Table  1 . Broadly speaking, sites B, E, G and H aligned with the “respiratory follow-up” model and sites F and I aligned with the “rehabilitation” model. Sites A and J had a high-volume, multi-tiered service whose community tier aligned with the “holistic GP assessment” model (site C above) and which also offered a hospital-based, rehabilitation-focused tier. The small service in Scotland (site D) had evolved from an initial respiratory focus to become part of the infectious diseases (ME/CFS) service; Lyme disease (another infectious disease whose sequelae include chronic fatigue) was also prevalent in this region.

The patient experience

Whilst the 10 participating clinics were very diverse in staffing, ethos and patient flows, the 29 patient interviews described remarkably consistent clinic experiences. Almost all identified the biggest problem to be the extended wait of several months before they were seen and the limited awareness (when initially referred) of what long covid clinics could provide. Some talked of how they cried with relief when they finally received an appointment. When the quality improvement collaborative was initially established, waiting times and bottlenecks were patients’ the top priority for quality improvement, and this ranking was shared by clinic staff, who were very aware of how much delays and uncertainties in assessment and treatment compounded patients’ suffering. This issue resolved to a large extent over the study period in all clinics as the referral backlog cleared and the incidence of new cases of long covid fell [ 85 ]; it will be covered in more detail in a separate publication.

Most patients in our sample were satisfied with the care they received when they were finally seen in clinic, especially how they finally felt “heard” after a clinician took a full history. They were relieved to receive affirmation of their experience, a diagnosis of what was wrong and reassurance that they were believed. They were grateful for the input of different members of the multidisciplinary teams and commented on the attentiveness, compassion and skill of allied professionals in particular (“she was wonderful, she got me breathing again”—patient BIR145 talking about a physical therapist). One or two patient participants expressed confusion about who exactly they had seen and what advice they had been given, and some did not realize that a telephone assessment had been an actual clinical consultation. A minority expressed disappointment that an expected investigation had not been ordered (one commented that they had not had any blood tests at all). Several had assumed that the help and advice from the long covid clinic would continue to be offered until they were better and were disappointed that they had been discharged after completing the various courses on offer (since their clinic had been set up as an “assessment only” service).

In the next sections, we give examples of topics raised in the quality improvement collaborative and how they were addressed.

Example quality topic 1: Outcome measures

The first topic considered by the quality improvement collaborative was how (that is, using which measures and metrics) to assess and monitor patients with long covid. In the absence of a validated biomarker, various symptom scores and quality of life scales—both generic and disease-specific—were mooted. Site F had already developed and validated a patient-reported outcome measure (PROM), the C19-YRS (Covid-19 Yorkshire Rehabilitation Scale) and used it for both research and clinical purposes [ 86 ]. It was quickly agreed that, for the purposes of generating comparative research findings across the ten clinics, the C19-YRS should be used at all sites and completed by patients three-monthly. A commercial partner produced an electronic version of this instrument and an app for patient smartphones. The quality improvement collaborative also agreed that patients should be asked to complete the EUROQOL EQ5D, a widely used generic health-related quality of life scale [ 87 ], in order to facilitate comparisons between long covid and other chronic conditions.

In retrospect, the discussions which led to the unopposed adoption of these two measures as a “quality” initiative in clinical care were somewhat aspirational. A review of progress at a subsequent quality improvement meeting revealed considerable variation among clinics, with a wide variety of measures used in different clinics to different degrees. Reasons for this variation were multiple. First, although our patient advisory group were keen that we should gather as much data as possible on the patient experience of this new condition, many clinic patients found the long questionnaires exhausting to complete due to cognitive impairment and fatigue. In addition, whilst patients were keen to answer questions on symptoms that troubled them, many had limited patience to fill out repeated surveys on symptoms that did not trouble them (“it almost felt as if I’ve not got long covid because I didn’t feel like I fit the criteria as they were laying it out”—patient SAL001). Staff assisted patients in completing the measures when needed, but this was time-consuming (up to 45 min per instrument) and burdensome for both staff and patients. In clinics where a high proportion of patients required assistance, staff time was the rate-limiting factor for how many instruments got completed. For some patients, one short instrument was the most that could be asked of them, and the clinician made a judgement on which one would be in their best interests on the day.

The second reason for variation was that the clinical diagnosis and management of particular features, complications and comorbidities of long covid required more nuance than was provided by these relatively generic instruments, and the level of detail sought varied with the specialist interest of the clinic (and the clinician). The modified C19-YRS [ 88 ], for example, contained 19 items, of which one asked about sleep quality. But if a patient had sleep difficulties, many clinicians felt that these needed to be documented in more detail—for example using the 8-item Epworth Sleepiness Scale, originally developed for conditions such as narcolepsy and obstructive sleep apnea [ 89 ]. The “Epworth score” was essential currency for referrals to some but not all specialist sleep services. Similarly, the C19-YRS had three items relating to anxiety, depression and post-traumatic stress disorder, but in clinics where there was a strong focus on mental health (e.g. when there was a resident psychologist), patients were usually invited to complete more specific tools (e.g. the Patient Health Questionnaire 9 [ 90 ], a 9-item questionnaire originally designed to assess severity of depression).

The third reason for variation was custom and practice. Ethnographic visits revealed that paper copies of certain instruments were routinely stacked on clinicians’ desks in outpatient departments and also (in some cases) handed out by administrative staff in waiting areas so that patients could complete them before seeing the clinician. These familiar clinic artefacts tended to be short (one-page) instruments that had a long tradition of use in clinical practice. They were not always fit for purpose. For example, the Nijmegen questionnaire was developed in the 1980s to assess hyperventilation; it was validated against a longer, “gold standard” instrument for that condition [ 91 ]. It subsequently became popular in respiratory clinics to diagnose or exclude breathing pattern disorder (a condition in which the normal physiological pattern of breathing becomes replaced with less efficient, shallower breathing [ 92 ]), so much so that the researchers who developed the instrument published a paper to warn fellow researchers that it had not been validated for this purpose [ 93 ]. Whilst a validated 17-item instrument for breathing pattern disorder (the Self-Evaluation of Breathing Questionnaire [ 94 ]) does exist, it is not in widespread clinical use. Most clinics in LOCOMOTION used Nijmegen either on all patients (e.g. as part of a comprehensive initial assessment, especially if the service had begun as a respiratory follow-up clinic) or when breathing pattern disorder was suspected.

In sum, the use of outcome measures in long covid clinics was a compromise between standardization and contingency. On the one hand, all clinics accepted the need to use “validated” instruments consistently. On the other hand, there were sometimes good reasons why they deviated from agreed practice, including mismatch between the clinic’s priorities as a research site, its priorities as a clinical service, and the particular clinical needs of a patient; the clinic’s—and the clinician’s—specialist focus; and long-held traditions of using particular instruments with which staff and patients were familiar.

Example quality topic 2: Postural orthostatic tachycardia syndrome (POTS)

Palpitations (common in long covid) and postural orthostatic tachycardia syndrome (POTS, a disproportionate acceleration in heart rate on standing, the assumed cause of palpitations in many long covid patients) was the top priority for quality improvement identified by our patient advisory group. Reflecting discussions and evidence (of various kinds) shared in online patient communities, the group were confident that POTS is common in long covid patients and that many cases remain undetected (perhaps misdiagnosed as anxiety). Their request that all long covid patients should be “screened” for POTS prompted a search for, and synthesis of, evidence (which we published in the BMJ [ 95 ]). In sum, that evidence was sparse and contested, but, combined with standard practice in specialist clinics, broadly supported the judicious use of the NASA Lean Test [ 96 ]. This test involves repeated measurements of pulse and blood pressure with the patient first lying and then standing (with shoulders resting against a wall).

The patient advisory group’s request that the NASA Lean Test should be conducted on all patients met with mixed responses from the clinics. In site F, the lead physician had an interest in autonomic dysfunction in chronic fatigue and was keen; he had already published a paper on how to adapt the NASA Lean Test for self-assessment at home [ 97 ]. Several other sites were initially opposed. Staff at site E, for example, offered various arguments:

The test is time-consuming, labor-intensive, and takes up space in the clinic which has an opportunity cost in terms of other potential uses;

The test is unvalidated and potentially misleading (there is a high incidence of both false negative and false positive results);

There is no proven treatment for POTS, so there is no point in testing for it;

It is a specialist test for a specialist condition, so it should be done in a specialist clinic where its benefits and limitations are better understood;

Objective testing does not change clinical management since what we treat is the patient’s symptoms (e.g. by a pragmatic trial of lifestyle measures and medication);

People with symptoms suggestive of dysautonomia have already been “triaged out” of this clinic (that is, identified in the initial telephone consultation and referred directly to neurology or cardiology);

POTS is a manifestation of the systemic nature of long covid; it does not need specific treatment but will improve spontaneously as the patient goes through standard interventions such as active pacing, respiratory physical therapy and sleep hygiene;

Testing everyone, even when asymptomatic, runs counter to the ethos of rehabilitation, which is to “de-medicalize” patients so as to better orient them to their recovery journey.

When clinics were invited to implement the NASA Lean Test on a consecutive sample of patients to resolve a dispute about the incidence of POTS (from “we’ve only seen a handful of people with it since the clinic began” to “POTS is common and often missed”), all but one site agreed to participate. The tertiary POTS centre linked to site H was already running the NASA Lean Test as standard on all patients. Site C, which operated entirely virtually, passed the work to the referring general practitioner by making this test a precondition for seeing the patient; site D, which was largely virtual, sent instructions for patients to self-administer the test at home.

The NASA Lean Test study has been published separately [ 98 ]. In sum, of 277 consecutive patients tested across the eight clinics, 20 (7%) had a positive NASA Lean Test for POTS and a further 28 (10%) a borderline result. Six of 20 patients who met the criteria for POTS on testing had no prior history of orthostatic intolerance. The question of whether this test should be used to “screen” all patients was not answered definitively. But the experience of participating in the study persuaded some sceptics that postural changes in heart rate could be severe in some long covid patients, did not appear to be fully explained by their previously held theories (e.g. “functional”, anxiety, deconditioning), and had likely been missed in some patients. The outcome of this particular quality improvement cycle was thus not a wholescale change in practice (for which the evidence base was weak) but a more subtle increase in clinical awareness, a greater willingness to consider testing for POTS and a greater commitment to contribute to research into this contested condition.

More generally, the POTS audit prompted some clinicians to recognize the value of quality improvement in novel clinical areas. One physician who had initially commented that POTS was not seen in their clinic, for example, reflected:

“ Our clinic population is changing. […] Overall there’s far fewer post-ICU patients with ECMO [extra-corporeal membrane oxygenation] issues and far more long covid from the community, and this is the bit our clinic isn’t doing so well on. We’re doing great on breathing pattern disorder; neuro[logists] are helping us with the brain fogs; our fatigue and occupational advice is ok but some of the dysautonomia symptoms that are more prevalent in the people who were not hospitalized – that’s where we need to improve .” -Respiratory physician, site G (from field visit 6.6.23)

Example quality topic 3: Management of fatigue

Fatigue was the commonest symptom overall and a high priority among both patients and clinicians for quality improvement. It often coexisted with the cluster of neurocognitive symptoms known as brain fog, with both conditions relapsing and remitting in step. Clinicians were keen to systematize fatigue management using a familiar clinical framework oriented around documenting a full clinical history, identifying associated symptoms, excluding or exploring comorbidities and alternative explanations (e.g. poor sleep patterns, depression, menopause, deconditioning), assessing how fatigue affects physical and mental function, implementing a program of physical and cognitive therapy that was sensitive to the patient’s condition and confidence level, and monitoring progress using validated patient-reported outcome measures and symptom diaries.

The underpinning logic of this approach, which broadly reflected World Health Organization guidance [ 99 ], was that fatigue and linked cognitive impairment could be a manifestation of many—perhaps interacting—conditions but that a whole-patient (body and mind) rehabilitation program was the cornerstone of management in most cases. Discussion in the quality improvement collaborative focused on issues such as whether fatigue was so severe that it produced safety concerns (e.g. in a person’s job or with childcare), the pros and cons of particular online courses such as yoga, relaxation and mindfulness (many were viewed positively, though the evidence base was considered weak), and the extent to which respiratory physical therapy had a crossover impact on fatigue (systematic reviews suggested that it may do, but these reviews also cautioned that primary studies were sparse, methodologically flawed, and heterogeneous [ 100 , 101 ]). They also debated the strengths and limitations of different fatigue-specific outcome measures, each of which had been developed and validated in a different condition, with varying emphasis on cognitive fatigue, physical fatigue, effect on daily life, and motivation. These instruments included the Modified Fatigue Impact Scale; Fatigue Severity Scale [ 102 ]; Fatigue Assessment Scale; Functional Assessment Chronic Illness Therapy—Fatigue (FACIT-F) [ 103 ]; Work and Social Adjustment Scale [ 104 ]; Chalder Fatigue Scale [ 105 ]; Visual Analogue Scale—Fatigue [ 106 ]; and the EQ5D [ 87 ]. In one clinic (site F), three of these scales were used in combination for reasons discussed below.

Some clinicians advocated melatonin or nutritional supplements (such as vitamin D or folic acid) for fatigue on the grounds that many patients found them helpful and formal placebo-controlled trials were unlikely ever to be conducted. But neurostimulants used in other fatigue-predominant conditions (e.g. brain injury, stroke), which also lacked clinical trial evidence in long covid, were viewed as inappropriate in most patients because of lack of evidence of clear benefit and hypothetical risk of harm (e.g. adverse drug reactions, polypharmacy).

Whilst the patient advisory group were broadly supportive of a whole-patient rehabilitative approach to fatigue, their primary concern was fatiguability , especially post-exertional symptom exacerbation (PESE, also known as “crashes”). In these, the patient becomes profoundly fatigued some hours or days after physical or mental exertion, and this state can last for days or even weeks [ 107 ]. Patients viewed PESE as a “red flag” symptom which they felt clinicians often missed and sometimes caused. They wanted the quality improvement effort to focus on ensuring that all clinicians were aware of the risks of PESE and acted accordingly. A discussion among patients and clinicians at a quality improvement collaborative meeting raised a new research hypothesis—that reducing the number of repeated episodes of PESE may improve the natural history of long covid.

These tensions around fatigue management played out differently in different clinics. In site C (the GP-led virtual clinic run from a community hub), fatigue was viewed as one manifestation of a whole-patient condition. The lead general practitioner used the metaphor of untangling a skein of wool: “you have to find the end and then gently pull it”. The underlying problem in a fatigued patient, for example, might be an undiagnosed physical condition such as anaemia, disturbed sleep, or inadequate pacing. These required (respectively) the chronic fatigue service (comprising an occupational therapist and specialist psychologist and oriented mainly to teaching the techniques of goal-setting and pacing), a “tiredness” work-up (e.g. to exclude anaemia or menopause), investigation of poor sleep (which, not uncommonly, was due to obstructive sleep apnea), and exploration of mental health issues.

In site G (a hospital clinic which had evolved from a respiratory service), patients with fatigue went through a fatigue management program led by the occupational therapist with emphasis on pacing, energy conservation, avoidance of PESE and sleep hygiene. Those without ongoing respiratory symptoms were often discharged back to their general practitioner once they had completed this; there was no consultant follow-up of unresolved fatigue.

In site F (a rehabilitation clinic which had a longstanding interest in chronic fatigue even before the pandemic), active interdisciplinary management of fatigue was commenced at or near the patient’s first visit, on the grounds that the earlier this began, the more successful it would be. In this clinic, patients were offered a more intensive package: a similar occupational therapy-led fatigue course as those in site G, plus input from a dietician to advise on regular balanced meals and caffeine avoidance and a group-based facilitated peer support program which centred on fatigue management. The dietician spoke enthusiastically about how improving diet in longstanding long covid patients often improved fatigue (e.g. because they had often lost muscle mass and tended to snack on convenience food rather than make meals from scratch), though she agreed there was no evidence base from trials to support this approach.

Pursuing local quality improvement through MDTs

Whilst some long covid patients had “textbook” symptoms and clinical findings, many cases were unique and some were fiendishly complex. One clinician commented that, somewhat paradoxically, “easy cases” were often the post-ICU follow-ups who had resolving chest complications; they tended to do well with a course of respiratory physical therapy and a return-to-work program. Such cases were rarely brought to MDT meetings. “Difficult cases” were patients who had not been hospitalized for their acute illness but presented with a months- or years-long history of multiple symptoms with fatigue typically predominant. Each one was different, as the following example (some details of which have been fictionalized to protect anonymity) illustrates.

The MDT is discussing Mrs Fermah, a 65-year-old homemaker who had covid-19 a year ago. She has had multiple symptoms since, including fluctuating fatigue, brain fog, breathlessness, retrosternal chest pain of burning character, dry cough, croaky voice, intermittent rashes (sometimes on eating), lips going blue, ankle swelling, orthopnoea, dizziness with the room spinning which can be triggered by stress, low back pain, aches and pains in the arms and legs and pins and needles in the fingertips, loss of taste and smell, palpitations and dizziness (unclear if postural, but clear association with nausea), headaches on waking, and dry mouth. She is somewhat overweight (body mass index 29) and admits to low mood. Functionally, she is mostly confined to the house and can no longer manage the stairs so has begun to sleep downstairs. She has stumbled once or twice but not fallen. Her social life has ceased and she rarely has the energy to see her grandchildren. Her 70-year-old husband is retired and generally supportive, though he spends most evenings at his club. Comorbidities include glaucoma which is well controlled and overseen by an ophthalmologist, mild club foot (congenital) and stage 1 breast cancer 20 years ago. Various tests, including a chest X-ray, resting and exercise oximetry and a blood panel, were normal except for borderline vitamin D level. Her breathing questionnaire score suggests she does not have breathing pattern disorder. ECG showed first-degree atrioventricular block and left axis deviation. No clinician has witnessed the blue lips. Her current treatment is online group respiratory physical therapy; a home visit is being arranged to assess her climbing stairs. She has declined a psychologist assessment. The consultant asks the nurse who assessed her: “Did you get a feel if this is a POTS-type dizziness or an ENT-type?” She sighs. “Honestly it was hard to tell, bless her.”—Site A MDT

This patient’s debilitating symptoms and functional impairments could all be due to long covid, yet “evidence-based” guidance for how to manage her complex suffering does not exist and likely never will exist. The question of which (if any) additional blood or imaging tests to do, in what order of priority, and what interventions to offer the patient will not be definitively answered by consulting clinical trials involving hundreds of patients, since (even if these existed) the decision involves weighing this patient’s history and the multiple factors and uncertainties that are relevant in her case. The knowledge that will help the MDT provide quality care to Mrs Fermah is case-based knowledge—accumulated clinical experience and wisdom from managing and deliberating on multiple similar cases. We consider case-based knowledge further in the “ Discussion ”.

Summary of key findings

This study has shown that a quality improvement collaborative of UK long covid clinics made some progress towards standardizing assessment and management in some topics, but some variation remained. This could be explained in part by the fact that different clinics had different histories and path dependencies, occupied a different place in the local healthcare ecosystem, served different populations, were differently staffed, and had different clinical interests. Our patient advisory group and clinicians in the quality improvement collaborative broadly prioritized the same topics for improvement but interpreted them somewhat differently. “Quality” long covid care had multiple dimensions, relating to (among other things) service set-up and accessibility, clinical provision appropriate to the patient’s need (including options for referral to other services locally), the human qualities of clinical and support staff, how knowledge was distributed across (and accessible within) the system, and the accumulated collective wisdom of local MDTs in dealing with complex cases (including multiple kinds of specialist expertise as well as relational knowledge of what was at stake for the patient). Whilst both staff and patients were keen to contribute to the quality improvement effort, the burden of measurement was evident: multiple outcome measures, used repeatedly, were resource-intensive for staff and exhausting for patients.

Strengths and limitations of this study

To our knowledge, we are the first to report both a quality improvement collaborative and an in-depth qualitative study of clinical work in long covid. Key strengths of this work include the diverse sampling frame (with sites from three UK jurisdictions and serving widely differing geographies and demographics); the use of documents, interviews and reflexive interpretive ethnography to produce meaningful accounts of how clinics emerged and how they were currently organized; the use of philosophical concepts to analyse data on how MDTs produced quality care on a patient-by-patient basis; and the close involvement of patient co-researchers and coauthors during the research and writing up.

Limitations of the study include its exclusive UK focus (the external validity of findings to other healthcare systems is unknown); the self-selecting nature of participants in a quality improvement collaborative (our patient advisory group suggested that the MDTs observed in this study may have represented the higher end of a quality spectrum, hence would be more likely than other MDTs to adhere to guidelines); and the particular perspective brought by the researchers (two GPs, a physical therapist and one non-clinical person) in ethnographic observations. Hospital specialists or organizational scholars, for example, may have noticed different things or framed what they observed differently.

Explaining variation in long covid care

Sutherland and Levesque’s framework mentioned in the “ Background ” section does not explain much of the variation found in our study [ 70 ]. In terms of capacity, at the time of this study most participating clinics benefited from ring-fenced resources. In terms of evidence, guidelines existed and were not greatly contested, but as illustrated by the case of Mrs Fermah above, many patients were exceptions to the guideline because of complex symptomatology and relevant comorbidities. In terms of agency, clinicians in most clinics were passionately engaged with long covid (they were pioneers who had set up their local clinic and successfully bid for national ring-fenced resources) and were generally keen to support patient choice (though not if the patient requested tests which were unavailable or deemed not indicated).

Astma et al.’s list of factors that may explain variation in practice (see “ Background ”) includes several that may be relevant to long covid, especially that the definition of appropriate care in this condition remains somewhat contested. But lack of opportunity to discuss cases was not a problem in the clinics in our sample. On the contrary, MDT meetings in each locality gave clinicians multiple opportunities to discuss cases with colleagues and reflect collectively on whether and how to apply particular guidelines.

The key problem was not that clinicians disputed the guidelines for managing long covid or were unaware of them; it was that the guidelines were not self-interpreting . Rather, MDTs had to deliberate on the balance of benefits and harms in different aspects of individual cases. In patients whose symptoms suggested a possible diagnosis of POTS (or who suspected themselves of having POTS), for example, these deliberations were sometimes lengthy and nuanced. Should a test result that is not technically in the abnormal range but close to it be treated as diagnostic, given that symptoms point to this diagnosis? If not, should the patient be told that the test excludes POTS or that it is equivocal? If a cardiology opinion has stated firmly that the patient does not have POTS but the cardiologist is not known for their interest in this condition, should a second specialist opinion be sought? If the gold standard “tilt test” [ 108 ] for POTS (usually available only in tertiary centres) is not available locally, does this patient merit a costly out-of-locality referral? Should the patient’s request for a trial of off-label medication, reflecting discussions in an online support group, be honoured? These are the kinds of questions on which MDTs deliberated at length.

The fact that many cases required extensive deliberation does not necessarily justify variation in practice among clinics. But taking into account the clinics’ very different histories, set-up, and local referral pathways, the variation begins to make sense. A patient who is being assessed in a clinic that functions as a specialist chronic fatigue centre and attracts referrals which reflect this interest (e.g. site F in our sample) will receive different management advice from one that functions as a telephone-only generalist assessment centre and refers on to other specialties (site C in our sample). The wide variation in case mix, coupled with the fact that a different proportion of these cases were highly complex in each clinic (and in different ways), suggests that variation in practice may reflect appropriate rather than inappropriate care.

Our patient advisory group affirmed that many of the findings reported here resonated with their own experience, but they raised several concerns. These included questions about patient groups who may have been missed in our sample because they were rarely discussed in MDTs. The decision to take a case to MDT discussion is taken largely by a clinician, and there was evidence from online support groups that some patients’ requests for their case to be taken to an MDT had been declined (though not, to our knowledge, in the clinics participating in the LOCOMOTION study).

We began this study by asking “what is quality in long covid care?”. We initially assumed that this question referred to a generalizable evidence base, which we felt we could identify, and we believed that we could then determine whether long covid clinics were following the evidence base through conventional audits of structure, process, and outcome. In retrospect, these assumptions were somewhat naïve. On the basis of our findings, we suggest that a better (and more individualized) research question might be “to what extent does each patient with long covid receive evidence-based care appropriate to their needs?”. This question would require individual case review on a sample of cases, tracking each patient longitudinally including cross-referrals, and also interviewing the patient.

Nomothetic versus idiographic knowledge

In a series of lectures first delivered in the 1950s and recently republished [ 109 ], psychiatrist Dr Maurice O’Connor Drury drew on the later philosophy of his friend and mentor Ludwig Wittgenstein to challenge what he felt was a concerning trend: that the nomothetic (generalizable, abstract) knowledge from randomized controlled trials (RCTs) was coming to over-ride the idiographic (personal, situated) knowledge about particular patients. Based on Wittgenstein’s writings on the importance of the particular, Drury predicted—presciently—that if implemented uncritically, RCTs would result in worse, not better, care for patients, since it would go hand-in-hand with a downgrading of experience, intuition, subjective judgement, personal reflection, and collective deliberation.

Much conventional quality improvement methodology is built on an assumption that nomothetic knowledge (for example, findings from RCTs and systematic reviews) is a higher form of knowing than idiographic knowledge. But idiographic, case-based reasoning—despite its position at the very bottom of evidence-based medicine’s hierarchy of evidence [ 110 ]—is a legitimate and important element of medical practice. Bioethicist Kathryn Montgomery, drawing on Aristotle’s notion of praxis , considers clinical practice to be an example of case-based reasoning [ 111 ]. Medicine is governed not by hard and fast laws but by competing maxims or rules of thumb ; the essence of judgement is deciding which (if any) rule should be applied in a particular circumstance. Clinical judgement incorporates science (especially the results of well-conducted research) and makes use of available tools and technologies (including guidelines and decision-support algorithms that incorporate research findings). But rather than being determined solely by these elements, clinical judgement is guided both by the scientific evidence and by the practical and ethical question “what is it best to do, for this individual, given these circumstances?”.

In this study, we observed clinical management of, and MDT deliberations on, hundreds of clinical cases. In the more straightforward ones (for example, recovering pneumonitis), guideline-driven care was not difficult to implement and such cases were rarely brought to the MDT. But cases like Mrs Fermah (see last section of “ Results ”) required much discussion on which aspects of which guideline were in the patient’s best interests to bring into play at any particular stage in their illness journey.

Conclusions

One systematic review on quality improvement collaboratives concluded that “ [those] reporting success generally addressed relatively straightforward aspects of care, had a strong evidence base and noted a clear evidence-practice gap in an accepted clinical pathway or guideline” (page 226) [ 60 ]. The findings from this study suggest that to the extent that such collaboratives address clinical cases that are not straightforward, conventional quality improvement methods may be less useful and even counterproductive.

The question “what is quality in long covid care?” is partly a philosophical one. Our findings support an approach that recognizes and values idiographic knowledge —including establishing and protecting a safe and supportive space for deliberation on individual cases to occur and to value and draw upon the collective learning that occurs in these spaces. It is through such deliberation that evidence-based guidelines can be appropriately interpreted and applied to the unique needs and circumstances of individual patients. We suggest that Drury’s warning about the limitations of nomothetic knowledge should prompt a reassessment of policies that rely too heavily on such knowledge, resulting in one-size-fits-all protocols. We also cautiously hypothesize that the need to centre the quality improvement effort on idiographic rather than nomothetic knowledge is unlikely to be unique to long covid. Indeed, such an approach may be particularly important in any condition that is complex, unpredictable, variable in presentation and clinical course, and associated with comorbidities.

Availability of data and materials

Selected qualitative data (ensuring no identifiable information) will be made available to formal research teams on reasonable request to Professor Greenhalgh at the University of Oxford, on condition that they have research ethics approval and relevant expertise. The quantitative data on NASA Lean Test have been published in full in a separate paper [ 98 ].

Abbreviations

Chronic fatigue syndrome

Intensive care unit

Jenny Ceolta-Smith

Julie Darbyshire

LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS

Multidisciplinary team

Myalgic encephalomyelitis

Middle East Respiratory Syndrome

National Aeronautics and Space Association

Occupational therapy/ist

Post-exertional symptom exacerbation

Postural orthostatic tachycardia syndrome

Speech and language therapy

Severe Acute Respiratory Syndrome

Trisha Greenhalgh

United Kingdom

United States

World Health Organization

Perego E, Callard F, Stras L, Melville-JÛhannesson B, Pope R, Alwan N. Why the Patient-Made Term “Long Covid” is needed. Wellcome Open Res. 2020;5:224.

Article   Google Scholar  

Greenhalgh T, Sivan M, Delaney B, Evans R, Milne R: Long covid—an update for primary care. bmj 2022;378:e072117.

Centers for Disease Control and Prevention (US): Long COVID or Post-COVID Conditions (updated 16th December 2022). Atlanta: CDC. Accessed 2nd June 2023 at https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html ; 2022.

National Institute for Health and Care Excellence (NICE) Scottish Intercollegiate Guidelines Network (SIGN) and Royal College of General Practitioners (RCGP): COVID-19 rapid guideline: managing the long-term effects of COVID-19, vol. Accessed 30th January 2022 at https://www.nice.org.uk/guidance/ng188/resources/covid19-rapid-guideline-managing-the-longterm-effects-of-covid19-pdf-51035515742 . London: NICE; 2022.

Organization WH: Post Covid-19 Condition (updated 7th December 2022), vol. Accessed 2nd June 2023 at https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition#:~:text=It%20is%20defined%20as%20the,months%20with%20no%20other%20explanation . Geneva: WHO; 2022.

Office for National Statistics: Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 31st March 2023. London: ONS. Accessed 30th May 2023 at https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/alldatarelatingtoprevalenceofongoingsymptomsfollowingcoronaviruscovid19infectionintheuk ; 2023.

Crook H, Raza S, Nowell J, Young M, Edison P: Long covid—mechanisms, risk factors, and management. bmj 2021;374.

Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, Pujol JC, Klaser K, Antonelli M, Canas LS. Attributes and predictors of long COVID. Nat Med. 2021;27(4):626–31.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Reese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, Laraway B, Antonescu C, Coleman B, Gargano M: Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine 2023;87.

Thaweethai T, Jolley SE, Karlson EW, Levitan EB, Levy B, McComsey GA, McCorkell L, Nadkarni GN, Parthasarathy S, Singh U. Development of a definition of postacute sequelae of SARS-CoV-2 infection. JAMA. 2023;329(22):1934–46.

Brown DA, O’Brien KK. Conceptualising Long COVID as an episodic health condition. BMJ Glob Health. 2021;6(9): e007004.

Article   PubMed   Google Scholar  

Tate WP, Walker MO, Peppercorn K, Blair AL, Edgar CD. Towards a Better Understanding of the Complexities of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID. Int J Mol Sci. 2023;24(6):5124.

Ahmed H, Patel K, Greenwood DC, Halpin S, Lewthwaite P, Salawu A, Eyre L, Breen A, Connor RO, Jones A. Long-term clinical outcomes in survivors of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome coronavirus (MERS) outbreaks after hospitalisation or ICU admission: a systematic review and meta-analysis. J Rehabil Med. 2020;52(5):1–11.

Google Scholar  

World Health Organisation: Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected: Interim guidance (13th March 2020). Geneva: WHO. Accessed 3rd January 2023 at https://t.co/JpNdP8LcV8?amp=1 ; 2020.

Rushforth A, Ladds E, Wieringa S, Taylor S, Husain L, Greenhalgh T: Long Covid – the illness narratives. Under review for Sociology of Health and Illness 2021.

Russell D, Spence NJ. Chase J-AD, Schwartz T, Tumminello CM, Bouldin E: Support amid uncertainty: Long COVID illness experiences and the role of online communities. SSM-Qual Res Health. 2022;2: 100177.

Article   PubMed   PubMed Central   Google Scholar  

Ziauddeen N, Gurdasani D, O’Hara ME, Hastie C, Roderick P, Yao G, Alwan NA. Characteristics and impact of Long Covid: Findings from an online survey. PLoS ONE. 2022;17(3): e0264331.

Evans RA, McAuley H, Harrison EM, Shikotra A, Singapuri A, Sereno M, Elneima O, Docherty AB, Lone NI, Leavy OC. Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study. Lancet Respir Med. 2021;9(11):1275–87.

Sykes DL, Holdsworth L, Jawad N, Gunasekera P, Morice AH, Crooks MG. Post-COVID-19 symptom burden: what is long-COVID and how should we manage it? Lung. 2021;199(2):113–9.

Altmann DM, Whettlock EM, Liu S, Arachchillage DJ, Boyton RJ: The immunology of long COVID. Nat Rev Immunol 2023:1–17.

Klein J, Wood J, Jaycox J, Dhodapkar RM, Lu P, Gehlhausen JR, Tabachnikova A, Greene K, Tabacof L, Malik AA et al : Distinguishing features of Long COVID identified through immune profiling. Nature 2023.

Chen B, Julg B, Mohandas S, Bradfute SB. Viral persistence, reactivation, and mechanisms of long COVID. Elife. 2023;12: e86015.

Wang C, Ramasamy A, Verduzco-Gutierrez M, Brode WM, Melamed E. Acute and post-acute sequelae of SARS-CoV-2 infection: a review of risk factors and social determinants. Virol J. 2023;20(1):124.

Cervia-Hasler C, Brüningk SC, Hoch T, Fan B, Muzio G, Thompson RC, Ceglarek L, Meledin R, Westermann P, Emmenegger M et al Persistent complement dysregulation with signs of thromboinflammation in active Long Covid Science 2024;383(6680):eadg7942.

Sivan M, Greenhalgh T, Darbyshire JL, Mir G, O’Connor RJ, Dawes H, Greenwood D, O’Connor D, Horton M, Petrou S. LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK. BMJ Open. 2022;12(5): e063505.

Rushforth A, Ladds E, Wieringa S, Taylor S, Husain L, Greenhalgh T. Long covid–the illness narratives. Soc Sci Med. 2021;286: 114326.

National Institute for Health and Care Excellence: COVID-19 rapid guideline: managing the long-term effects of COVID-19, vol. Accessed 4th October 2023 at https://www.nice.org.uk/guidance/ng188/resources/covid19-rapid-guideline-managing-the-longterm-effects-of-covid19-pdf-51035515742 . London: NICE 2020.

NHS England: Long COVID: the NHS plan for 2021/22. London: NHS England. Accessed 2nd August 2022 at https://www.england.nhs.uk/coronavirus/documents/long-covid-the-nhs-plan-for-2021-22/ ; 2021.

NHS England: NHS to offer ‘long covid’ sufferers help at specialist centres. London: NHS England. Accessed 10th October 2020 at https://www.england.nhs.uk/2020/10/nhs-to-offer-long-covid-help/ ; 2020 (7th October).

NHS England: The NHS plan for improving long COVID services, vol. Acessed 4th February 2024 at https://www.england.nhs.uk/publication/the-nhs-plan-for-improving-long-covid-services/ .London: Gov.uk; 2022.

NHS England: Commissioning guidance for post-COVID services for adults, children and young people, vol. Accessed 6th February 2024 at https://www.england.nhs.uk/long-read/commissioning-guidance-for-post-covid-services-for-adults-children-and-young-people/ . London: gov.uk; 2023.

National Institute for Health Research: Researching Long Covid: Adressing a new global health challenge, vol. Accessed 9.8.23 at https://evidence.nihr.ac.uk/collection/researching-long-covid-addressing-a-new-global-health-challenge/ . London: NIHR; 2022.

Subbaraman N. NIH will invest $1 billion to study long COVID. Nature. 2021;591(7850):356–356.

Article   CAS   PubMed   Google Scholar  

Donabedian A. The definition of quality and approaches to its assessment and monitoring. Ann Arbor: Michigan; 1980.

Laffel G, Blumenthal D. The case for using industrial quality management science in health care organizations. JAMA. 1989;262(20):2869–73.

Maxwell RJ. Quality assessment in health. BMJ. 1984;288(6428):1470.

Berwick DM, Godfrey BA, Roessner J. Curing health care: New strategies for quality improvement. The Journal for Healthcare Quality (JHQ). 1991;13(5):65–6.

Deming WE. Out of the Crisis. Cambridge, MA: MIT Press; 1986.

Argyris C: Increasing leadership effectiveness: New York: J. Wiley; 1976.

Juran JM: A history of managing for quality: The evolution, trends, and future directions of managing for quality: Asq Press; 1995.

Institute of Medicine (US): Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001.

McNab D, McKay J, Shorrock S, Luty S, Bowie P. Development and application of ‘systems thinking’ principles for quality improvement. BMJ Open Qual. 2020;9(1): e000714.

Sampath B, Rakover J, Baldoza K, Mate K, Lenoci-Edwards J, Barker P. ​Whole-System Quality: A Unified Approach to Building Responsive, Resilient Health Care Systems. Boston: Institute for Healthcare Immprovement; 2021.

Batalden PB, Davidoff F: What is “quality improvement” and how can it transform healthcare? In . , vol. 16: BMJ Publishing Group Ltd; 2007: 2–3.

Baker G. Collaborating for improvement: the Institute for Healthcare Improvement’s breakthrough series. New Med. 1997;1:5–8.

Plsek PE. Collaborating across organizational boundaries to improve the quality of care. Am J Infect Control. 1997;25(2):85–95.

Ayers LR, Beyea SC, Godfrey MM, Harper DC, Nelson EC, Batalden PB. Quality improvement learning collaboratives. Qual Manage Healthcare. 2005;14(4):234–47.

Brandrud AS, Schreiner A, Hjortdahl P, Helljesen GS, Nyen B, Nelson EC. Three success factors for continual improvement in healthcare: an analysis of the reports of improvement team members. BMJ Qual Saf. 2011;20(3):251–9.

Dückers ML, Spreeuwenberg P, Wagner C, Groenewegen PP. Exploring the black box of quality improvement collaboratives: modelling relations between conditions, applied changes and outcomes. Implement Sci. 2009;4(1):1–12.

Nadeem E, Olin SS, Hill LC, Hoagwood KE, Horwitz SM. Understanding the components of quality improvement collaboratives: a systematic literature review. Milbank Q. 2013;91(2):354–94.

Shortell SM, Marsteller JA, Lin M, Pearson ML, Wu S-Y, Mendel P, Cretin S, Rosen M: The role of perceived team effectiveness in improving chronic illness care. Medical Care 2004:1040–1048.

Wilson T, Berwick DM, Cleary PD. What do collaborative improvement projects do? Experience from seven countries. Joint Commission J Qual Safety. 2004;30:25–33.

Schouten LM, Hulscher ME, van Everdingen JJ, Huijsman R, Grol RP. Evidence for the impact of quality improvement collaboratives: systematic review. BMJ. 2008;336(7659):1491–4.

Hulscher ME, Schouten LM, Grol RP, Buchan H. Determinants of success of quality improvement collaboratives: what does the literature show? BMJ Qual Saf. 2013;22(1):19–31.

Dixon-Woods M, Bosk CL, Aveling EL, Goeschel CA, Pronovost PJ. Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.

Bate P, Mendel P, Robert G: Organizing for quality: the improvement journeys of leading hospitals in Europe and the United States: CRC Press; 2007.

Andersson-Gäre B, Neuhauser D. The health care quality journey of Jönköping County Council. Sweden Qual Manag Health Care. 2007;16(1):2–9.

Törnblom O, Stålne K, Kjellström S. Analyzing roles and leadership in organizations from cognitive complexity and meaning-making perspectives. Behav Dev. 2018;23(1):63.

Greenhalgh T, Russell J. Why Do Evaluations of eHealth Programs Fail? An Alternative Set of Guiding Principles. PLoS Med. 2010;7(11): e1000360.

Wells S, Tamir O, Gray J, Naidoo D, Bekhit M, Goldmann D. Are quality improvement collaboratives effective? A systematic review. BMJ Qual Saf. 2018;27(3):226–40.

Landon BE, Wilson IB, McInnes K, Landrum MB, Hirschhorn L, Marsden PV, Gustafson D, Cleary PD. Effects of a quality improvement collaborative on the outcome of care of patients with HIV infection: the EQHIV study. Ann Intern Med. 2004;140(11):887–96.

Mittman BS. Creating the evidence base for quality improvement collaboratives. Ann Intern Med. 2004;140(11):897–901.

Wennberg JE. Unwarranted variations in healthcare delivery: implications for academic medical centres. BMJ. 2002;325(7370):961–4.

Bungay H. Cancer and health policy: the postcode lottery of care. Soc Policy Admin. 2005;39(1):35–48.

Wennberg JE, Cooper MM: The Quality of Medical Care in the United States: A Report on the Medicare Program: The Dartmouth Atlas of Health Care 1999: The Center for the Evaluative Clinical Sciences [Internet]. 1999.

DaSilva P, Gray JM. English lessons: can publishing an atlas of variation stimulate the discussion on appropriateness of care? Med J Aust. 2016;205(S10):S5–7.

Gray WK, Day J, Briggs TW, Harrison S. Identifying unwarranted variation in clinical practice between healthcare providers in England: Analysis of administrative data over time for the Getting It Right First Time programme. J Eval Clin Pract. 2021;27(4):743–50.

Wabe N, Thomas J, Scowen C, Eigenstetter A, Lindeman R, Georgiou A. The NSW Pathology Atlas of Variation: Part I—Identifying Emergency Departments With Outlying Laboratory Test-Ordering Practices. Ann Emerg Med. 2021;78(1):150–62.

Jamal A, Babazono A, Li Y, Fujita T, Yoshida S, Kim SA. Elucidating variations in outcomes among older end-stage renal disease patients on hemodialysis in Fukuoka Prefecture, Japan. PLoS ONE. 2021;16(5): e0252196.

Sutherland K, Levesque JF. Unwarranted clinical variation in health care: definitions and proposal of an analytic framework. J Eval Clin Pract. 2020;26(3):687–96.

Tanenbaum SJ. Reducing variation in health care: The rhetorical politics of a policy idea. J Health Polit Policy Law. 2013;38(1):5–26.

Atsma F, Elwyn G, Westert G. Understanding unwarranted variation in clinical practice: a focus on network effects, reflective medicine and learning health systems. Int J Qual Health Care. 2020;32(4):271–4.

Horbar JD, Rogowski J, Plsek PE, Delmore P, Edwards WH, Hocker J, Kantak AD, Lewallen P, Lewis W, Lewit E. Collaborative quality improvement for neonatal intensive care. Pediatrics. 2001;107(1):14–22.

Van Maanen J: Tales of the field: On writing ethnography: University of Chicago Press; 2011.

Golden-Biddle K, Locke K. Appealing work: An investigation of how ethnographic texts convince. Organ Sci. 1993;4(4):595–616.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Glaser BG. The constant comparative method of qualitative analysis. Soc Probl. 1965;12:436–45.

Willis R. The use of composite narratives to present interview findings. Qual Res. 2019;19(4):471–80.

Vojdani A, Vojdani E, Saidara E, Maes M. Persistent SARS-CoV-2 Infection, EBV, HHV-6 and other factors may contribute to inflammation and autoimmunity in long COVID. Viruses. 2023;15(2):400.

Choutka J, Jansari V, Hornig M, Iwasaki A. Unexplained post-acute infection syndromes. Nat Med. 2022;28(5):911–23.

Connors JM, Ariëns RAS. Uncertainties about the roles of anticoagulation and microclots in postacute sequelae of severe acute respiratory syndrome coronavirus 2 infection. J Thromb Haemost. 2023;21(10):2697–701.

Patel MA, Knauer MJ, Nicholson M, Daley M, Van Nynatten LR, Martin C, Patterson EK, Cepinskas G, Seney SL, Dobretzberger V. Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism. Mol Med. 2022;28(1):122.

Greenhalgh T, Sivan M, Delaney B, Evans R, Milne R: Long covid—an update for primary care. bmj 2022, 378.

Parkin A, Davison J, Tarrant R, Ross D, Halpin S, Simms A, Salman R, Sivan M. A multidisciplinary NHS COVID-19 service to manage post-COVID-19 syndrome in the community. J Prim Care Commun Health. 2021;12:21501327211010990.

NHS England: COVID-19 Post-Covid Assessment Service, vol. Accessed 5th March 2024 at https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-post-covid-assessment-service/ . London: NHS England; 2024.

Sivan M, Halpin S, Gee J, Makower S, Parkin A, Ross D, Horton M, O'Connor R: The self-report version and digital format of the COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) for Long Covid or Post-COVID syndrome assessment and monitoring. Adv Clin Neurosci Rehabil 2021;20(3).

The EuroQol Group. EuroQol-a new facility for the measurement of health-related quality of life. Health Policy. 1990;16(3):199–208.

Sivan M, Preston NJ, Parkin A, Makower S, Gee J, Ross D, Tarrant R, Davison J, Halpin S, O’Connor RJ, et al. The modified COVID-19 Yorkshire Rehabilitation Scale (C19-YRSm) patient-reported outcome measure for Long Covid or Post-COVID syndrome. J Med Virol. 2022;94(9):4253–64.

Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14(6):540–5.

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

Van Dixhoorn J, Duivenvoorden H. Efficacy of Nijmegen Questionnaire in recognition of the hyperventilation syndrome. J Psychosom Res. 1985;29(2):199–206.

Evans R, Pick A, Lardner R, Masey V, Smith N, Greenhalgh T: Breathing difficulties after covid-19: a guide for primary care. BMJ 2023;381.

Van Dixhoorn J, Folgering H: The Nijmegen Questionnaire and dysfunctional breathing. In . , vol. 1: Eur Respiratory Soc; 2015.

Courtney R, Greenwood KM. Preliminary investigation of a measure of dysfunctional breathing symptoms: The Self Evaluation of Breathing Questionnaire (SEBQ). Int J Osteopathic Med. 2009;12(4):121–7.

Espinosa-Gonzalez A, Master H, Gall N, Halpin S, Rogers N, Greenhalgh T. Orthostatic tachycardia after covid-19. BMJ (Clinical Research ed). 2023;380:e073488–e073488.

PubMed   Google Scholar  

Bungo M, Charles J, Johnson P Jr. Cardiovascular deconditioning during space flight and the use of saline as a countermeasure to orthostatic intolerance. Aviat Space Environ Med. 1985;56(10):985–90.

CAS   PubMed   Google Scholar  

Sivan M, Corrado J, Mathias C. The Adapted Autonomic Profile (Aap) Home-Based Test for the Evaluation of Neuro-Cardiovascular Autonomic Dysfunction. Adv Clin Neurosci Rehabil. 2022;3:10–13. https://doi.org/10.47795/QKBU46715 .

Lee C, Greenwood DC, Master H, Balasundaram K, Williams P, Scott JT, Wood C, Cooper R, Darbyshire JL, Gonzalez AE. Prevalence of orthostatic intolerance in long covid clinic patients and healthy volunteers: A multicenter study. J Med Virol. 2024;96(3): e29486.

World Health Organization: Clinical management of covid-19 - living guideline. Geneva: WHO. Accessed 4th October 2023 at https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-2 ; 2023.

Ahmed I, Mustafaoglu R, Yeldan I, Yasaci Z, Erhan B: Effect of pulmonary rehabilitation approaches on dyspnea, exercise capacity, fatigue, lung functions and quality of life in patients with COVID-19: A Systematic Review and Meta-Analysis. Arch Phys Med Rehabil 2022.

Dillen H, Bekkering G, Gijsbers S, Vande Weygaerde Y, Van Herck M, Haesevoets S, Bos DAG, Li A, Janssens W, Gosselink R, et al. Clinical effectiveness of rehabilitation in ambulatory care for patients with persisting symptoms after COVID-19: a systematic review. BMC Infect Dis. 2023;23(1):419.

Learmonth Y, Dlugonski D, Pilutti L, Sandroff B, Klaren R, Motl R. Psychometric properties of the fatigue severity scale and the modified fatigue impact scale. J Neurol Sci. 2013;331(1–2):102–7.

Webster K, Cella D, Yost K. The Functional Assessment of Chronic Illness T herapy (FACIT) Measurement System: properties, applications, and interpretation. Health Qual Life Outcomes. 2003;1(1):1–7.

Mundt JC, Marks IM, Shear MK, Greist JM. The Work and Social Adjustment Scale: a simple measure of impairment in functioning. Br J Psychiatry. 2002;180(5):461–4.

Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, Wallace E. Development of a fatigue scale. J Psychosom Res. 1993;37(2):147–53.

Shahid A, Wilkinson K, Marcu S, Shapiro CM: Visual analogue scale to evaluate fatigue severity (VAS-F). In: STOP, THAT and one hundred other sleep scales . edn.: Springer; 2011:399–402.

Parker M, Sawant HB, Flannery T, Tarrant R, Shardha J, Bannister R, Ross D, Halpin S, Greenwood DC, Sivan M. Effect of using a structured pacing protocol on post-exertional symptom exacerbation and health status in a longitudinal cohort with the post-COVID-19 syndrome. J Med Virol. 2023;95(1): e28373.

Kenny RA, Bayliss J, Ingram A, Sutton R. Head-up tilt: a useful test for investigating unexplained syncope. The Lancet. 1986;327(8494):1352–5.

Drury MOC: Science and Psychology. In: The selected writings of Maurice O’Connor Drury: On Wittgenstein, philosophy, religion and psychiatry. edn.: Bloomsbury Publishing; 2017.

Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med. 2000;342(25):1887–92.

Mongtomery K: How doctors think: Clinical judgment and the practice of medicine: Oxford University Press; 2005.

Download references

Acknowledgements

We are grateful to clinic staff for allowing us to study their work and to patients for allowing us to sit in on their consultations. We also thank the funder of LOCOMOTION (National Institute for Health Research) and the patient advisory group for lived experience input.

This research is supported by National Institute for Health Research (NIHR) Long Covid Research Scheme grant (Ref COV-LT-0016).

Author information

Authors and affiliations.

Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Rd, Oxford, OX2 6GG, UK

Trisha Greenhalgh, Julie L. Darbyshire & Emma Ladds

Imperial College Healthcare NHS Trust, London, UK

LOCOMOTION Patient Advisory Group and Lived Experience Representative, London, UK

You can also search for this author in PubMed   Google Scholar

Contributions

TG conceptualized the overall study, led the empirical work, supported the quality improvement meetings, conducted the ethnographic visits, led the data analysis, developed the theorization and wrote the first draft of the paper. JLD organized and led the quality improvement meetings, supported site-based researchers to collect and analyse data on their clinic, collated and summarized data on quality topics, and liaised with the patient advisory group. CL conceptualized and led the quality topic on POTS, including exploring reasons for some clinics’ reluctance to conduct testing and collating and analysing the NASA Lean Test data across all sites. EL assisted with ethnographic visits, data analysis, and theorization. JCS contributed lived experience of long covid and also clinical experience as an occupational therapist; she liaised with the wider patient advisory group, whose independent (patient-led) audit of long covid clinics informed the quality improvement prioritization exercise. All authors provided extensive feedback on drafts and contributed to discussions and refinements. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Trisha Greenhalgh .

Ethics declarations

Ethics approval and consent to participate.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276) and subsequent amendments.

Patient participants in clinic were approached by the clinician (without the researcher present) and gave verbal informed consent for a clinically qualified researcher to observe the consultation. If they consented, the researcher was then invited to sit in. A written record was made in field notes of this verbal consent. It was impractical to seek consent from patients whose cases were discussed (usually with very brief clinical details) in online MDTs. Therefore, clinical case examples from MDTs presented in the paper are fictionalized cases constructed from multiple real cases and with key clinical details changed (for example, comorbidities were replaced with different conditions which would produce similar symptoms). All fictionalized cases were checked by our patient advisory group to check that they were plausible to lived experience experts.

Consent for publication

No direct patient cases are reported in this manuscript. For details of how the fictionalized cases were constructed and validated, see “Consent to participate” above.

Competing interests

TG was a member of the UK National Long Covid Task Force 2021–2023 and on the Oversight Group for the NICE Guideline on Long Covid 2021–2022. She is a member of Independent SAGE.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Greenhalgh, T., Darbyshire, J.L., Lee, C. et al. What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography. BMC Med 22 , 159 (2024). https://doi.org/10.1186/s12916-024-03371-6

Download citation

Received : 04 December 2023

Accepted : 26 March 2024

Published : 15 April 2024

DOI : https://doi.org/10.1186/s12916-024-03371-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Post-covid-19 syndrome
  • Quality improvement
  • Breakthrough collaboratives
  • Warranted variation
  • Unwarranted variation
  • Improvement science
  • Ethnography
  • Idiographic reasoning
  • Nomothetic reasoning

BMC Medicine

ISSN: 1741-7015

what is research aims and objectives

IMAGES

  1. 21 Research Objectives Examples (Copy and Paste)

    what is research aims and objectives

  2. Formulating Research Aims and Objectives

    what is research aims and objectives

  3. Study Blog: CRP

    what is research aims and objectives

  4. Research Aim and Objectives

    what is research aims and objectives

  5. How to define Research Objectives?

    what is research aims and objectives

  6. Research Aims and Objectives: The dynamic duo for successful research

    what is research aims and objectives

VIDEO

  1. HEADMAN CHIGODORA NDAKAPISWA SIKARUDZI

  2. Research Aim, Objectives and Questions Mar 13, 2024

  3. The smart principle Research Aims and Objectives part 5

  4. How to Write Objectives in Research Proposal

  5. CHAPTER 1: Introduction

  6. COMMON PITFALLS Research Aims and Objectives part 3

COMMENTS

  1. Aims and Objectives

    The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved. Research aims are relatively broad; research objectives are specific. Research aims focus on a project's long-term outcomes; research objectives focus on its immediate, short-term outcomes.

  2. Research Questions, Objectives & Aims (+ Examples)

    Research Aims: Examples. True to the name, research aims usually start with the wording "this research aims to…", "this research seeks to…", and so on. For example: "This research aims to explore employee experiences of digital transformation in retail HR.". "This study sets out to assess the interaction between student ...

  3. Research Objectives

    Research aims. A distinction is often made between research objectives and research aims. A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

  4. What Are Research Objectives and How to Write Them (with Examples)

    Research studies have a research question, research hypothesis, and one or more research objectives. A research question is what a study aims to answer, and a research hypothesis is a predictive statement about the relationship between two or more variables, which the study sets out to prove or disprove.

  5. What's the difference between research aims and objectives?

    A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement, before your research objectives. Research objectives are more specific than your research aim. They indicate the specific ways you'll address the overarching aim.

  6. Formulating Research Aims and Objectives

    Formulating research aim and objectives in an appropriate manner is one of the most important aspects of your thesis. This is because research aim and objectives determine the scope, depth and the overall direction of the research. Research question is the central question of the study that has to be answered on the basis of research findings.

  7. Research Aims and Objectives: The dynamic duo for successful ...

    Research aims and objectives are the foundation of any research project. They provide a clear direction and purpose for the study, ensuring that you stay focused and on track throughout the process. They are your trusted navigational tools, leading you to success. Understanding the relationship between research objectives and aims is crucial to ...

  8. What is a Research Objective? Definition, Types, Examples and Best

    A research objective is defined as a clear and concise statement of the specific goals and aims of a research study. It outlines what the researcher intends to accomplish and what they hope to learn or discover through their research. Research objectives are crucial for guiding the research process and ensuring that the study stays focused and ...

  9. Research Objectives

    Research Objectives. Research objectives refer to the specific goals or aims of a research study. They provide a clear and concise description of what the researcher hopes to achieve by conducting the research.The objectives are typically based on the research questions and hypotheses formulated at the beginning of the study and are used to guide the research process.

  10. Research Aims and Objectives

    The Takeaway. The research aim is the primary focus of the research. Research aims are related to research objectives. The objectives determine in what ways that purpose will be achieved. Research aim needs to answer the "what", "why" and "how" questions of the research. Research objectives need to be SMART.

  11. A Guide to Writing Research Objectives and Aims

    Aim focus on what a project proposes to achieve; objectives focus on how the project will achieve its goal. The research objectives are more specific than the research aims. Objectives focus on the short-term and immediate outcomes of a project while aim focus on its long-term outcomes. It would be best to write an objective as a numbered list ...

  12. Develop the research objectives (Chapter 1)

    Summary. The importance of research aims and objectives cannot be over-stressed. It is vital to have a very clear understanding of what the research is about and what you are actually trying to achieve. You need to know this. And you need to be able to communicate it to others. Carrying out a research project is rather like going on a journey.

  13. Defining Research Objectives: How To Write Them

    However, sticking to research objectives isn't always easy, especially in broad or unconventional research. This is why most researchers follow the SMART criteria when defining their research objectives. Understanding SMART Criteria in Research. Think of research objectives as a roadmap to achieving your research goals, with the SMART ...

  14. How to write a research proposal?

    Aims and objectives. The research purpose (or goal or aim) gives a broad indication of what the researcher wishes to achieve in the research. The hypothesis to be tested can be the aim of the study. The objectives related to parameters or tools used to achieve the aim are generally categorised as primary and secondary objectives.

  15. How to Write a Research Proposal

    A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement, before your research objectives. Research objectives are more specific than your research aim. They indicate the specific ways you'll address the overarching aim.

  16. Research Questions, Objectives & Aims (+ Examples)

    Research Objectives: What are they? The researching objectives take the explore aims also makes them more practical both actionable.In other words, this research objectives showcase who measures that the researcher is take to achieve to research aims.. The research objectives need to be way more specific (higher resolution) and actionsable than the research aims.

  17. How to Write the Aims and Objectives

    Writing objectives. The objectives describe how you would achieve your research aim. You can do this through the following steps, The first one to two objectives can be applied to the literature review. (Verbs to be used: investigate, examine, study) One objective can be applied to the methodology portion.

  18. Essential Ingredients of a Good Research Proposal for Undergraduate and

    The research aim and objectives or hypotheses that are based on the research problem or question(s) considered above will tell the reader what exactly the researcher intends or wants to investigate. This section offers the researcher the opportunity to explain how the research will be carried out.

  19. Research questions, hypotheses and objectives

    Research objective. ... Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand ...

  20. What's the difference between research aims and objectives?

    A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement, before your research objectives. Research objectives are more specific than your research aim. They indicate the specific ways you'll address the overarching aim.

  21. SAGE Research Methods: Find resources to answer your research methods

    Click to continue

  22. Research Objectives: What They Are and How to Write Them

    Research objectives are integral to the research framework as the nexus between the research problem, questions, and hypotheses. They translate the broad goals of your study into actionable steps, ensuring every aspect of your research is purposefully aligned towards addressing the research problem.

  23. Research Objectives: Definition and How To Write Them

    Research objectives are the outcomes that you aim to achieve by conducting research. Many research projects contain more than one research objective. Creating strong research objectives can help your organization achieve its overall goals. The purpose of research objectives is to drive the research project, including data collection, analysis ...

  24. What is quality in long covid care? Lessons from a national quality

    Long covid (post covid-19 condition) is a complex condition with diverse manifestations, uncertain prognosis and wide variation in current approaches to management. There have been calls for formal quality standards to reduce a so-called "postcode lottery" of care. The original aim of this study—to examine the nature of quality in long covid care and reduce unwarranted variation in ...

  25. How Republicans view their party and key issues ...

    Nearly three-quarters of Republicans and Republican leaners (73%) expressed a favorable view of the high court in a survey conducted last August, shortly after a term that included the overturning of Roe v. Wade. That was 8 points higher than in January 2022. By contrast, just 28% of Democrats and Democratic leaners viewed the Supreme Court ...