what is research question in article

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How to Write a Research Question

What is a research question? A research question is the question around which you center your research. It should be:

• clear : it provides enough specifics that one’s audience can easily understand its purpose without needing additional explanation.
• focused : it is narrow enough that it can be answered thoroughly in the space the writing task allows.
• concise : it is expressed in the fewest possible words.
• complex : it is not answerable with a simple “yes” or “no,” but rather requires synthesis and analysis of ideas and sources prior to composition of an answer.
• arguable : its potential answers are open to debate rather than accepted facts.

You should ask a question about an issue that you are genuinely curious and/or passionate about.

The question you ask should be developed for the discipline you are studying. A question appropriate for Biology, for instance, is different from an appropriate one in Political Science or Sociology. If you are developing your question for a course other than first-year composition, you may want to discuss your ideas for a research question with your professor.

Why is a research question essential to the research process? Research questions help writers focus their research by providing a path through the research and writing process. The specificity of a well-developed research question helps writers avoid the “all-about” paper and work toward supporting a specific, arguable thesis.

Steps to developing a research question:

• Choose an interesting general topic. Most professional researchers focus on topics they are genuinely interested in studying. Writers should choose a broad topic about which they genuinely would like to know more. An example of a general topic might be “Slavery in the American South” or “Films of the 1930s.”
• Do some preliminary research on your general topic. Do a few quick searches in current periodicals and journals on your topic to see what’s already been done and to help you narrow your focus. What issues are scholars and researchers discussing, when it comes to your topic? What questions occur to you as you read these articles?
• Consider your audience. For most college papers, your audience will be academic, but always keep your audience in mind when narrowing your topic and developing your question. Would that particular audience be interested in the question you are developing?
• Start asking questions. Taking into consideration all of the above, start asking yourself open-ended “how” and “why” questions about your general topic. For example, “Why were slave narratives effective tools in working toward the abolishment of slavery?” or “How did the films of the 1930s reflect or respond to the conditions of the Great Depression?”
• Is your research question clear? With so much research available on any given topic, research questions must be as clear as possible in order to be effective in helping the writer direct his or her research.
• Is your research question focused? Research questions must be specific enough to be well covered in the space available.
• Is your research question complex? Research questions should not be answerable with a simple “yes” or “no” or by easily-found facts.  They should, instead, require both research and analysis on the part of the writer. They often begin with “How” or “Why.”
• Begin your research . After you’ve come up with a question, think about the possible paths your research could take. What sources should you consult as you seek answers to your question? What research process will ensure that you find a variety of perspectives and responses to your question?

Sample Research Questions

Unclear: How should social networking sites address the harm they cause? Clear: What action should social networking sites like MySpace and Facebook take to protect users’ personal information and privacy? The unclear version of this question doesn’t specify which social networking sites or suggest what kind of harm the sites might be causing. It also assumes that this “harm” is proven and/or accepted. The clearer version specifies sites (MySpace and Facebook), the type of potential harm (privacy issues), and who may be experiencing that harm (users). A strong research question should never leave room for ambiguity or interpretation. Unfocused: What is the effect on the environment from global warming? Focused: What is the most significant effect of glacial melting on the lives of penguins in Antarctica?

The unfocused research question is so broad that it couldn’t be adequately answered in a book-length piece, let alone a standard college-level paper. The focused version narrows down to a specific effect of global warming (glacial melting), a specific place (Antarctica), and a specific animal that is affected (penguins). It also requires the writer to take a stance on which effect has the greatest impact on the affected animal. When in doubt, make a research question as narrow and focused as possible.

Too simple: How are doctors addressing diabetes in the U.S.? Appropriately Complex:   What main environmental, behavioral, and genetic factors predict whether Americans will develop diabetes, and how can these commonalities be used to aid the medical community in prevention of the disease?

The simple version of this question can be looked up online and answered in a few factual sentences; it leaves no room for analysis. The more complex version is written in two parts; it is thought provoking and requires both significant investigation and evaluation from the writer. As a general rule of thumb, if a quick Google search can answer a research question, it’s likely not very effective.

Last updated 8/8/2018

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How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 
• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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Where to Put the Research Question in a Paper

Silke Haidekker has a PhD in Pharmacology from the University of Hannover. She is a Clinical Research Associate in multiple pharmaceutical companies in Germany and the USA. She now works as a full-time medical translator and writer in a small town in Georgia.

Of Rats and Panic Attacks: A Doctoral Student’s Tale

You would probably agree that the time spent writing your PhD dissertation or thesis is not only a time of taking pride or even joy in what you do, but also a time riddled with panic attacks of different varieties and lengths. When I worked on my PhD thesis in pharmacology in Germany many years back, I had  my  first panic attack as I first learned how to kill rats for my experiments with a very ugly tool called a guillotine! After that part of the procedure, I was to remove and mash their livers, spike them with Ciclosporin A (an immunosuppressive agent), and then present the metabolites by high-pressure liquid chromatography.

Many rats later, I had another serious panic attack. It occurred at the moment my doctoral adviser told me to write my first research paper on the Ciclosporin A metabolites I had detected in hundreds of slimy mashes of rat liver. Sadly, this second panic attack led to a third one that was caused by living in the pre-internet era, when it was not as easy to access information about  how to write research papers .

How I got over writing my first research paper is now ancient history. But it was only years later, living in the USA and finally being immersed in the language of most scientific research papers, that my interest in the art of writing “good” research papers was sparked during conferences held by the  American Medical Writers Association , as well as by getting involved in different writing programs and academic self-study courses.

How to State the Research Question in the Introduction Section

Good writing begins with clearly stating your research question (or hypothesis) in the Introduction section —the focal point on which your entire paper builds and unfolds in the subsequent Methods, Results, and Discussion sections . This research question or hypothesis that goes into the first section of your research manuscript, the Introduction, explains at least three major elements:

a) What is  known  or believed about the research topic?

B) what is still  unknown  (or problematic), c) what is the  question or hypothesis  of your investigation.

Some medical writers refer to this organizational structure of the Introduction as a “funnel shape” because it starts broadly, with the bigger picture, and then follows one scientifically logical step after the other until finally narrowing down the story to the focal point of your research at the end of the funnel.

Let’s now look in greater detail at a research question example and how you can logically embed it into the Introduction to make it a powerful focal point and ignite the reader’s interest about the importance of your research:

a) The Known

You should start by giving your reader a brief overview of knowledge or previous studies already performed in the context of your research topic.

The topic of one of my research papers was “investigating the value of diabetes as an independent predictor of death in people with end-stage renal disease (ESRD).” So in the Introduction, I first presented the basic knowledge that diabetes is the leading cause of end-stage renal disease (ESRD) and thus made the reader better understand our interest in this specific study population. I then presented previous studies already showing that diabetes indeed seems to represent an independent risk factor for death in the general population. However, very few studies had been performed in the ESRD population and those only yielded controversial results.

Example :  “It seems well established that there is a link between diabetic nephropathy and hypertensive nephropathy and end-stage renal disease (ESRD) in Western countries. In 2014, 73% of patients in US hospitals had comorbid ESRD and type 2 diabetes (1, 2, 3)…”

b) The Unknown

In our example, this “controversy” flags the “unknown” or “problematic” and therefore provides strong reasons for why further research is justified. The unknown should be clearly stated or implied by using phrases such as “were controversial” (as in our example), “…has not been determined,” or “…is unclear.” By clearly stating what is “unknown,” you indicate that your research is new. This creates a smooth transition into your research question.

Example :  “However, previous studies have failed to isolate diabetes as an independent factor, and thus much remains unknown about specific risk factors associated with both diabetes and ESRD .”

c) The Research Question (Hypothesis)

Your research question is the question that inevitably evolves from the deficits or problems revealed in the “Unknown” and clearly states the goal of your research. It is important to describe your research question in just one or two short sentences, but very precisely and including all variables studied, if applicable. A transition should be used to mark the transition from the unknown to the research question using one word such as “therefore” or “accordingly,” or short phrases like “for this reason” or “considering this lack of crucial information.”

In our example, we stated the research question as follows:

Example :  “Therefore, the primary goal of our study was to perform a Kaplan-Meier survival study and to investigate, by means of the Cox proportional hazard model, the value of diabetes as an independent predictor of death in diabetic patients with ESRD.”

Note that the research question may include the  experimental approach  of the study used to answer the research question.

Another powerful way to introduce the research question is to  state the research question as a hypothesis  so that the reader can more easily anticipate the answer. In our case, the question could be put as follows:

Example :  “To test the hypothesis that diabetes is an independent predictor of death in people with ESRD, we performed a Kaplan-Survival study and investigated the value of diabetes by means of the Cox proportional hazard model.”

Note that this sentence leads with an introductory clause that indicates the hypothesis itself, transitioning well into a synopsis of the approach in the second half of the sentence.

The generic framework of the Introduction can be modified to include, for example,  two  research questions instead of just one. In such a case, both questions must follow inevitably from the previous statements, meaning that the background information leading to the second question cannot be omitted. Otherwise, the Introduction will get confusing, with the reader not knowing where that question comes from.

Begin with your research purpose in mind

To conclude, here is my simple but most important advice for you as a researcher preparing to write a scientific paper (or just the Introduction of a research paper) for the first time: Think your research question through precisely before trying to write it down; have in mind the reasons for exactly why you wanted to do this specific research, what exactly you wanted to find out, and how (by which methods) you did your investigation. If you have the answers to these questions in mind (or even better, create a comprehensive outline ) before starting the paper, the actual writing process will be a piece of cake and you will finish it “like a rat up a drainpipe”! And hopefully with no panic attacks.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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How To Write a Research Question

Academic writing and research require a distinct focus and direction. A well-designed research question gives purpose and clarity to your research. In addition, it helps your readers understand the issue you are trying to address and explore.

Every time you want to know more about a subject, you will pose a question. The same idea is used in research as well. You must pose a question in order to effectively address a research problem. That's why the research question is an integral part of the research process. Additionally, it offers the author writing and reading guidelines, be it qualitative research or quantitative research.

In your research paper , you must single out just one issue or problem. The specific issue or claim you wish to address should be included in your thesis statement in order to clarify your main argument.

A good research question must have the following characteristics.

• Should include only one problem in the research question
• Should be able to find the answer using primary data and secondary data sources
• Should be possible to resolve within the given time and other constraints
• Detailed and in-depth results should be achievable
• Should be relevant and realistic.
• It should relate to your chosen area of research

While a larger project, like a thesis, might have several research questions to address, each one should be directed at your main area of study. Of course, you can use different research designs and research methods (qualitative research or quantitative research) to address various research questions. However, they must all be pertinent to the study's objectives.

What is a Research Question?

A research question is an inquiry that the research attempts to answer. It is the heart of the systematic investigation. Research questions are the most important step in any research project. In essence, it initiates the research project and establishes the pace for the specific research A research question is:

• Clear : It provides enough detail that the audience understands its purpose without any additional explanation.
• Focused : It is so specific that it can be addressed within the time constraints of the writing task.
• Succinct: It is written in the shortest possible words.
• Complex : It is not possible to answer it with a "yes" or "no", but requires analysis and synthesis of ideas before somebody can create a solution.
• Argumental : Its potential answers are open for debate rather than accepted facts.

A good research question usually focuses on the research and determines the research design, methodology, and hypothesis. It guides all phases of inquiry, data collection, analysis, and reporting. You should gather valuable information by asking the right questions.

Why are Research Questions so important?

Regardless of whether it is a qualitative research or quantitative research project, research questions provide writers and their audience with a way to navigate the writing and research process. Writers can avoid "all-about" papers by asking straightforward and specific research questions that help them focus on their research and support a specific thesis.

Types of Research Questions

There are two types of research: Qualitative research and Quantitative research . There must be research questions for every type of research. Your research question will be based on the type of research you want to conduct and the type of data collection.

The first step in designing research involves identifying a gap and creating a focused research question.

Below is a list of common research questions that can be used in a dissertation. Keep in mind that these are merely illustrations of typical research questions used in dissertation projects. The real research questions themselves might be more difficult.

Example Research Questions

The following are a few examples of research questions and research problems to help you understand how research questions can be created for a particular research problem.

Steps to Write Research Questions

You can focus on the issue or research gaps you're attempting to solve by using the research questions as a direction.

If you're unsure how to go about writing a good research question, these are the steps to follow in the process:

• Select an interesting topic Always choose a topic that interests you. Because if your curiosity isn’t aroused by a subject, you’ll have a hard time conducting research around it. Alos, it’s better that you pick something that’s neither too narrow or too broad.
• Do preliminary research on the topic Search for relevant literature to gauge what problems have already been tackled by scholars. You can do that conveniently through repositories like Scispace , where you’ll find millions of papers in one place. Once you do find the papers you’re looking for, try our reading assistant, SciSpace Copilot to get simple explanations for the paper . You’ll be able to quickly understand the abstract, find the key takeaways, and the main arguments presented in the paper. This will give you a more contextual understanding of your subject and you’ll have an easier time identifying knowledge gaps in your discipline.

     Also: ChatPDF vs. SciSpace Copilot: Unveiling the best tool for your research

• Consider your audience It is essential to understand your audience to develop focused research questions for essays or dissertations. When narrowing down your topic, you can identify aspects that might interest your audience.
• Ask questions Asking questions will give you a deeper understanding of the topic. Evaluate your question through the What, Why, When, How, and other open-ended questions assessment.
• Assess your question Once you have created a research question, assess its effectiveness to determine if it is useful for the purpose. Refine and revise the dissertation research question multiple times.

Additionally, use this list of questions as a guide when formulating your research question.

Are you able to answer a specific research question? After identifying a gap in research, it would be helpful to formulate the research question. And this will allow the research to solve a part of the problem. Is your research question clear and centered on the main topic? It is important that your research question should be specific and related to your central goal. Are you tackling a difficult research question? It is not possible to answer the research question with a simple yes or no. The problem requires in-depth analysis. It is often started with "How" and "Why."

Start your research Once you have completed your dissertation research questions, it is time to review the literature on similar topics to discover different perspectives.

Strong  Research Question Samples

Uncertain: How should social networking sites work on the hatred that flows through their platform?

Certain: What should social media sites like Twitter or Facebook do to address the harm they are causing?

This unclear question does not specify the social networking sites that are being used or what harm they might be causing. In addition, this question assumes that the "harm" has been proven and/or accepted. This version is more specific and identifies the sites (Twitter, Facebook), the type and extent of harm (privacy concerns), and who might be suffering from that harm (users). Effective research questions should not be ambiguous or interpreted.

Unfocused: What are the effects of global warming on the environment?

Focused: What are the most important effects of glacial melting in Antarctica on penguins' lives?

This broad research question cannot be addressed in a book, let alone a college-level paper. Focused research targets a specific effect of global heating (glacial  melting), an area (Antarctica), or a specific animal (penguins). The writer must also decide which effect will have the greatest impact on the animals affected. If in doubt, narrow down your research question to the most specific possible.

Too Simple: What are the U.S. doctors doing to treat diabetes?

Appropriately complex: Which factors, if any, are most likely to predict a person's risk of developing diabetes?

This simple version can be found online. It is easy to answer with a few facts. The second, more complicated version of this question is divided into two parts. It is thought-provoking and requires extensive investigation as well as evaluation by the author. So, ensure that a quick Google search should not answer your research question.

How to write a strong Research Question?

The foundation of all research is the research question. You should therefore spend as much time as necessary to refine your research question based on various data.

You can conduct your research more efficiently and analyze your results better if you have great research questions for your dissertation, research paper , or essay .

The following criteria can help you evaluate the strength and importance of your research question and can be used to determine the strength of your research question:

• Researchable
• It should only cover one issue.
• A subjective judgment should not be included in the question.
• It can be answered with data analysis and research.
• Specific and Practical
• It should not contain a plan of action, policy, or solution.
• It should be clearly defined
• Within research limits
• Complex and Arguable
• It shouldn't be difficult to answer.
• To find the truth, you need in-depth knowledge
• Allows for discussion and deliberation
• Original and Relevant
• It should be in your area of study
• Its results should be measurable
• It should be original

Conclusion - How to write Research Questions?

Research questions provide a clear guideline for research. One research question may be part of a larger project, such as a dissertation. However, each question should only focus on one topic.

Research questions must be answerable, practical, specific, and applicable to your field. The research type that you use to base your research questions on will determine the research topic. You can start by selecting an interesting topic and doing preliminary research. Then, you can begin asking questions, evaluating your questions, and start your research.

Now it's easier than ever to streamline your research workflow with SciSpace ResearchGPT . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, read, write and publish their research and fosters collaboration.

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Article Contents

Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

• Article contents
• Figures & tables
• Supplementary Data

Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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• Research Questions: Definitions, Types + [Examples]

Research questions lie at the core of systematic investigation and this is because recording accurate research outcomes is tied to asking the right questions. Asking the right questions when conducting research can help you collect relevant and insightful information that ultimately influences your work, positively. 

The right research questions are typically easy to understand, straight to the point, and engaging. In this article, we will share tips on how to create the right research questions and also show you how to create and administer an online questionnaire with Formplus . 

What is a Research Question? 

A research question is a specific inquiry which the research seeks to provide a response to. It resides at the core of systematic investigation and it helps you to clearly define a path for the research process. 

A research question is usually the first step in any research project. Basically, it is the primary interrogation point of your research and it sets the pace for your work.  

Typically, a research question focuses on the research, determines the methodology and hypothesis, and guides all stages of inquiry, analysis, and reporting. With the right research questions, you will be able to gather useful information for your investigation. 

Types of Research Questions 

Research questions are broadly categorized into 2; that is, qualitative research questions and quantitative research questions. Qualitative and quantitative research questions can be used independently and co-dependently in line with the overall focus and objectives of your research. 

If your research aims at collecting quantifiable data , you will need to make use of quantitative research questions. On the other hand, qualitative questions help you to gather qualitative data bothering on the perceptions and observations of your research subjects. 

Qualitative Research Questions  

A qualitative research question is a type of systematic inquiry that aims at collecting qualitative data from research subjects. The aim of qualitative research questions is to gather non-statistical information pertaining to the experiences, observations, and perceptions of the research subjects in line with the objectives of the investigation. 

Types of Qualitative Research Questions  

• Ethnographic Research Questions

As the name clearly suggests, ethnographic research questions are inquiries presented in ethnographic research. Ethnographic research is a qualitative research approach that involves observing variables in their natural environments or habitats in order to arrive at objective research outcomes. 

These research questions help the researcher to gather insights into the habits, dispositions, perceptions, and behaviors of research subjects as they interact in specific environments. 

Ethnographic research questions can be used in education, business, medicine, and other fields of study, and they are very useful in contexts aimed at collecting in-depth and specific information that are peculiar to research variables. For instance, asking educational ethnographic research questions can help you understand how pedagogy affects classroom relations and behaviors. 

This type of research question can be administered physically through one-on-one interviews, naturalism (live and work), and participant observation methods. Alternatively, the researcher can ask ethnographic research questions via online surveys and questionnaires created with Formplus.  

Examples of Ethnographic Research Questions

• Why do you use this product?
• Have you noticed any side effects since you started using this drug?
• Does this product meet your needs?

• Case Studies

A case study is a qualitative research approach that involves carrying out a detailed investigation into a research subject(s) or variable(s). In the course of a case study, the researcher gathers a range of data from multiple sources of information via different data collection methods, and over a period of time. 

The aim of a case study is to analyze specific issues within definite contexts and arrive at detailed research subject analyses by asking the right questions. This research method can be explanatory, descriptive , or exploratory depending on the focus of your systematic investigation or research. 

An explanatory case study is one that seeks to gather information on the causes of real-life occurrences. This type of case study uses “how” and “why” questions in order to gather valid information about the causative factors of an event. 

Descriptive case studies are typically used in business researches, and they aim at analyzing the impact of changing market dynamics on businesses. On the other hand, exploratory case studies aim at providing answers to “who” and “what” questions using data collection tools like interviews and questionnaires. 

Some questions you can include in your case studies are: 

• Why did you choose our services?
• How has this policy affected your business output?
• What benefits have you recorded since you started using our product?

An interview is a qualitative research method that involves asking respondents a series of questions in order to gather information about a research subject. Interview questions can be close-ended or open-ended , and they prompt participants to provide valid information that is useful to the research. 

An interview may also be structured, semi-structured , or unstructured , and this further influences the types of questions they include. Structured interviews are made up of more close-ended questions because they aim at gathering quantitative data while unstructured interviews consist, primarily, of open-ended questions that allow the researcher to collect qualitative information from respondents. 

You can conduct interview research by scheduling a physical meeting with respondents, through a telephone conversation, and via digital media and video conferencing platforms like Skype and Zoom. Alternatively, you can use Formplus surveys and questionnaires for your interview. 

Examples of interview questions include: 

• What challenges did you face while using our product?
• What specific needs did our product meet?
• What would you like us to improve our service delivery?

Quantitative Research Questions

Quantitative research questions are questions that are used to gather quantifiable data from research subjects. These types of research questions are usually more specific and direct because they aim at collecting information that can be measured; that is, statistical information. 

Types of Quantitative Research Questions

• Descriptive Research Questions

Descriptive research questions are inquiries that researchers use to gather quantifiable data about the attributes and characteristics of research subjects. These types of questions primarily seek responses that reveal existing patterns in the nature of the research subjects. 

It is important to note that descriptive research questions are not concerned with the causative factors of the discovered attributes and characteristics. Rather, they focus on the “what”; that is, describing the subject of the research without paying attention to the reasons for its occurrence. 

Descriptive research questions are typically closed-ended because they aim at gathering definite and specific responses from research participants. Also, they can be used in customer experience surveys and market research to collect information about target markets and consumer behaviors. 

Descriptive Research Question Examples

• How often do you make use of our fitness application?
• How much would you be willing to pay for this product?

• Comparative Research Questions

A comparative research question is a type of quantitative research question that is used to gather information about the differences between two or more research subjects across different variables. These types of questions help the researcher to identify distinct features that mark one research subject from the other while highlighting existing similarities. 

Asking comparative research questions in market research surveys can provide insights on how your product or service matches its competitors. In addition, it can help you to identify the strengths and weaknesses of your product for a better competitive advantage.  

The 5 steps involved in the framing of comparative research questions are: 

• Choose your starting phrase
• Identify and name the dependent variable
• Identify the groups you are interested in
• Identify the appropriate adjoining text
• Write out the comparative research question

Comparative Research Question Samples 

• What are the differences between a landline telephone and a smartphone?
• What are the differences between work-from-home and on-site operations?

• Relationship-based Research Questions  

Just like the name suggests, a relationship-based research question is one that inquires into the nature of the association between two research subjects within the same demographic. These types of research questions help you to gather information pertaining to the nature of the association between two research variables. 

Relationship-based research questions are also known as correlational research questions because they seek to clearly identify the link between 2 variables. 

Read: Correlational Research Designs: Types, Examples & Methods

Examples of relationship-based research questions include: 

• What is the relationship between purchasing power and the business site?
• What is the relationship between the work environment and workforce turnover?

Examples of a Good Research Question

Since research questions lie at the core of any systematic investigations, it is important to know how to frame a good research question. The right research questions will help you to gather the most objective responses that are useful to your systematic investigation. 

A good research question is one that requires impartial responses and can be answered via existing sources of information. Also, a good research question seeks answers that actively contribute to a body of knowledge; hence, it is a question that is yet to be answered in your specific research context.

• Open-Ended Questions

 An open-ended question is a type of research question that does not restrict respondents to a set of premeditated answer options. In other words, it is a question that allows the respondent to freely express his or her perceptions and feelings towards the research subject. 

Examples of Open-ended Questions

• How do you deal with stress in the workplace?
• What is a typical day at work like for you?
• Close-ended Questions

A close-ended question is a type of survey question that restricts respondents to a set of predetermined answers such as multiple-choice questions . Close-ended questions typically require yes or no answers and are commonly used in quantitative research to gather numerical data from research participants. 

Examples of Close-ended Questions

• Did you enjoy this event?
• How likely are you to recommend our services?
• Very Likely
• Somewhat Likely
• Likert Scale Questions

A Likert scale question is a type of close-ended question that is structured as a 3-point, 5-point, or 7-point psychometric scale . This type of question is used to measure the survey respondent’s disposition towards multiple variables and it can be unipolar or bipolar in nature. 

Example of Likert Scale Questions

• How satisfied are you with our service delivery?
• Very dissatisfied
• Not satisfied
• Very satisfied
• Rating Scale Questions

A rating scale question is a type of close-ended question that seeks to associate a specific qualitative measure (rating) with the different variables in research. It is commonly used in customer experience surveys, market research surveys, employee reviews, and product evaluations. 

Example of Rating Questions

• How would you rate our service delivery?

  Examples of a Bad Research Question

Knowing what bad research questions are would help you avoid them in the course of your systematic investigation. These types of questions are usually unfocused and often result in research biases that can negatively impact the outcomes of your systematic investigation. 

• Loaded Questions

A loaded question is a question that subtly presupposes one or more unverified assumptions about the research subject or participant. This type of question typically boxes the respondent in a corner because it suggests implicit and explicit biases that prevent objective responses. 

Example of Loaded Questions

• Have you stopped smoking?
• Where did you hide the money?
• Negative Questions

A negative question is a type of question that is structured with an implicit or explicit negator. Negative questions can be misleading because they upturn the typical yes/no response order by requiring a negative answer for affirmation and an affirmative answer for negation. 

Examples of Negative Questions

• Would you mind dropping by my office later today?
• Didn’t you visit last week?
• Leading Questions  

A l eading question is a type of survey question that nudges the respondent towards an already-determined answer. It is highly suggestive in nature and typically consists of biases and unverified assumptions that point toward its premeditated responses. 

Examples of Leading Questions

• If you enjoyed this service, would you be willing to try out our other packages?
• Our product met your needs, didn’t it?

Read More: Leading Questions: Definition, Types, and Examples

How to Use Formplus as Online Research Questionnaire Tool  

With Formplus, you can create and administer your online research questionnaire easily. In the form builder, you can add different form fields to your questionnaire and edit these fields to reflect specific research questions for your systematic investigation. 

Here is a step-by-step guide on how to create an online research questionnaire with Formplus: 

• Sign in to your Formplus accoun t, then click on the “create new form” button in your dashboard to access the Form builder.

• In the form builder, add preferred form fields to your online research questionnaire by dragging and dropping them into the form. Add a title to your form in the title block. You can edit form fields by clicking on the “pencil” icon on the right corner of each form field.

• Save the form to access the customization section of the builder. Here, you can tweak the appearance of your online research questionnaire by adding background images, changing the form font, and adding your organization’s logo.

• Finally, copy your form link and share it with respondents. You can also use any of the multiple sharing options available.

Conclusion  

The success of your research starts with framing the right questions to help you collect the most valid and objective responses. Be sure to avoid bad research questions like loaded and negative questions that can be misleading and adversely affect your research data and outcomes. 

Your research questions should clearly reflect the aims and objectives of your systematic investigation while laying emphasis on specific contexts. To help you seamlessly gather responses for your research questions, you can create an online research questionnaire on Formplus.  

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

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Home » Research Questions – Types, Examples and Writing Guide

Research Questions – Types, Examples and Writing Guide

Table of Contents

Research Questions

Definition:

Research questions are the specific questions that guide a research study or inquiry. These questions help to define the scope of the research and provide a clear focus for the study. Research questions are usually developed at the beginning of a research project and are designed to address a particular research problem or objective.

Types of Research Questions

Types of Research Questions are as follows:

Descriptive Research Questions

These aim to describe a particular phenomenon, group, or situation. For example:

• What are the characteristics of the target population?
• What is the prevalence of a particular disease in a specific region?

Exploratory Research Questions

These aim to explore a new area of research or generate new ideas or hypotheses. For example:

• What are the potential causes of a particular phenomenon?
• What are the possible outcomes of a specific intervention?

Explanatory Research Questions

These aim to understand the relationship between two or more variables or to explain why a particular phenomenon occurs. For example:

• What is the effect of a specific drug on the symptoms of a particular disease?
• What are the factors that contribute to employee turnover in a particular industry?

Predictive Research Questions

These aim to predict a future outcome or trend based on existing data or trends. For example :

• What will be the future demand for a particular product or service?
• What will be the future prevalence of a particular disease?

Evaluative Research Questions

These aim to evaluate the effectiveness of a particular intervention or program. For example:

• What is the impact of a specific educational program on student learning outcomes?
• What is the effectiveness of a particular policy or program in achieving its intended goals?

How to Choose Research Questions

Choosing research questions is an essential part of the research process and involves careful consideration of the research problem, objectives, and design. Here are some steps to consider when choosing research questions:

• Identify the research problem: Start by identifying the problem or issue that you want to study. This could be a gap in the literature, a social or economic issue, or a practical problem that needs to be addressed.
• Conduct a literature review: Conducting a literature review can help you identify existing research in your area of interest and can help you formulate research questions that address gaps or limitations in the existing literature.
• Define the research objectives : Clearly define the objectives of your research. What do you want to achieve with your study? What specific questions do you want to answer?
• Consider the research design : Consider the research design that you plan to use. This will help you determine the appropriate types of research questions to ask. For example, if you plan to use a qualitative approach, you may want to focus on exploratory or descriptive research questions.
• Ensure that the research questions are clear and answerable: Your research questions should be clear and specific, and should be answerable with the data that you plan to collect. Avoid asking questions that are too broad or vague.
• Get feedback : Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, feasible, and meaningful.

How to Write Research Questions

Guide for Writing Research Questions:

• Start with a clear statement of the research problem: Begin by stating the problem or issue that your research aims to address. This will help you to formulate focused research questions.
• Use clear language : Write your research questions in clear and concise language that is easy to understand. Avoid using jargon or technical terms that may be unfamiliar to your readers.
• Be specific: Your research questions should be specific and focused. Avoid broad questions that are difficult to answer. For example, instead of asking “What is the impact of climate change on the environment?” ask “What are the effects of rising sea levels on coastal ecosystems?”
• Use appropriate question types: Choose the appropriate question types based on the research design and objectives. For example, if you are conducting a qualitative study, you may want to use open-ended questions that allow participants to provide detailed responses.
• Consider the feasibility of your questions : Ensure that your research questions are feasible and can be answered with the resources available. Consider the data sources and methods of data collection when writing your questions.
• Seek feedback: Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, appropriate, and meaningful.

Examples of Research Questions

Some Examples of Research Questions with Research Titles:

Research Title: The Impact of Social Media on Mental Health

• Research Question : What is the relationship between social media use and mental health, and how does this impact individuals’ well-being?

Research Title: Factors Influencing Academic Success in High School

• Research Question: What are the primary factors that influence academic success in high school, and how do they contribute to student achievement?

Research Title: The Effects of Exercise on Physical and Mental Health

• Research Question: What is the relationship between exercise and physical and mental health, and how can exercise be used as a tool to improve overall well-being?

Research Title: Understanding the Factors that Influence Consumer Purchasing Decisions

• Research Question : What are the key factors that influence consumer purchasing decisions, and how do these factors vary across different demographics and products?

Research Title: The Impact of Technology on Communication

• Research Question : How has technology impacted communication patterns, and what are the effects of these changes on interpersonal relationships and society as a whole?

Research Title: Investigating the Relationship between Parenting Styles and Child Development

• Research Question: What is the relationship between different parenting styles and child development outcomes, and how do these outcomes vary across different ages and developmental stages?

Research Title: The Effectiveness of Cognitive-Behavioral Therapy in Treating Anxiety Disorders

• Research Question: How effective is cognitive-behavioral therapy in treating anxiety disorders, and what factors contribute to its success or failure in different patients?

Research Title: The Impact of Climate Change on Biodiversity

• Research Question : How is climate change affecting global biodiversity, and what can be done to mitigate the negative effects on natural ecosystems?

Research Title: Exploring the Relationship between Cultural Diversity and Workplace Productivity

• Research Question : How does cultural diversity impact workplace productivity, and what strategies can be employed to maximize the benefits of a diverse workforce?

Research Title: The Role of Artificial Intelligence in Healthcare

• Research Question: How can artificial intelligence be leveraged to improve healthcare outcomes, and what are the potential risks and ethical concerns associated with its use?

Applications of Research Questions

Here are some of the key applications of research questions:

• Defining the scope of the study : Research questions help researchers to narrow down the scope of their study and identify the specific issues they want to investigate.
• Developing hypotheses: Research questions often lead to the development of hypotheses, which are testable predictions about the relationship between variables. Hypotheses provide a clear and focused direction for the study.
• Designing the study : Research questions guide the design of the study, including the selection of participants, the collection of data, and the analysis of results.
• Collecting data : Research questions inform the selection of appropriate methods for collecting data, such as surveys, interviews, or experiments.
• Analyzing data : Research questions guide the analysis of data, including the selection of appropriate statistical tests and the interpretation of results.
• Communicating results : Research questions help researchers to communicate the results of their study in a clear and concise manner. The research questions provide a framework for discussing the findings and drawing conclusions.

Characteristics of Research Questions

Characteristics of Research Questions are as follows:

• Clear and Specific : A good research question should be clear and specific. It should clearly state what the research is trying to investigate and what kind of data is required.
• Relevant : The research question should be relevant to the study and should address a current issue or problem in the field of research.
• Testable : The research question should be testable through empirical evidence. It should be possible to collect data to answer the research question.
• Concise : The research question should be concise and focused. It should not be too broad or too narrow.
• Feasible : The research question should be feasible to answer within the constraints of the research design, time frame, and available resources.
• Original : The research question should be original and should contribute to the existing knowledge in the field of research.
• Significant : The research question should have significance and importance to the field of research. It should have the potential to provide new insights and knowledge to the field.
• Ethical : The research question should be ethical and should not cause harm to any individuals or groups involved in the study.

Purpose of Research Questions

Research questions are the foundation of any research study as they guide the research process and provide a clear direction to the researcher. The purpose of research questions is to identify the scope and boundaries of the study, and to establish the goals and objectives of the research.

The main purpose of research questions is to help the researcher to focus on the specific area or problem that needs to be investigated. They enable the researcher to develop a research design, select the appropriate methods and tools for data collection and analysis, and to organize the results in a meaningful way.

Research questions also help to establish the relevance and significance of the study. They define the research problem, and determine the research methodology that will be used to address the problem. Research questions also help to determine the type of data that will be collected, and how it will be analyzed and interpreted.

Finally, research questions provide a framework for evaluating the results of the research. They help to establish the validity and reliability of the data, and provide a basis for drawing conclusions and making recommendations based on the findings of the study.

Advantages of Research Questions

There are several advantages of research questions in the research process, including:

• Focus : Research questions help to focus the research by providing a clear direction for the study. They define the specific area of investigation and provide a framework for the research design.
• Clarity : Research questions help to clarify the purpose and objectives of the study, which can make it easier for the researcher to communicate the research aims to others.
• Relevance : Research questions help to ensure that the study is relevant and meaningful. By asking relevant and important questions, the researcher can ensure that the study will contribute to the existing body of knowledge and address important issues.
• Consistency : Research questions help to ensure consistency in the research process by providing a framework for the development of the research design, data collection, and analysis.
• Measurability : Research questions help to ensure that the study is measurable by defining the specific variables and outcomes that will be measured.
• Replication : Research questions help to ensure that the study can be replicated by providing a clear and detailed description of the research aims, methods, and outcomes. This makes it easier for other researchers to replicate the study and verify the results.

Limitations of Research Questions

Limitations of Research Questions are as follows:

• Subjectivity : Research questions are often subjective and can be influenced by personal biases and perspectives of the researcher. This can lead to a limited understanding of the research problem and may affect the validity and reliability of the study.
• Inadequate scope : Research questions that are too narrow in scope may limit the breadth of the study, while questions that are too broad may make it difficult to focus on specific research objectives.
• Unanswerable questions : Some research questions may not be answerable due to the lack of available data or limitations in research methods. In such cases, the research question may need to be rephrased or modified to make it more answerable.
• Lack of clarity : Research questions that are poorly worded or ambiguous can lead to confusion and misinterpretation. This can result in incomplete or inaccurate data, which may compromise the validity of the study.
• Difficulty in measuring variables : Some research questions may involve variables that are difficult to measure or quantify, making it challenging to draw meaningful conclusions from the data.
• Lack of generalizability: Research questions that are too specific or limited in scope may not be generalizable to other contexts or populations. This can limit the applicability of the study’s findings and restrict its broader implications.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Identifying your research question

Making informed decisions about what to study, and defining your research question, even within a predetermined field, is critical to a successful research career, and can be one of the hardest challenges for a scientist.

Being knowledgeable about the state of your field and up-to-date with recent developments can help you:

• Make decisions about  what to study within niche research areas
• Identify  top researchers  in your field whose work you can follow and potentially collaborate with
• Find  important journals to read regularly and publish in
• Explain to others  why your work is important by being able to recount the bigger picture

How can you identify a research question?

Reading regularly is the most common way of identifying a good research question. This enables you to keep up to date with recent advancements and identify certain issues or unsolved problems that keep appearing.

Begin by searching for and reading literature in your field. Start with  general interest  journals, but don’t limit yourself to journal publications only; you can also look for clues in the news or on research blogs. Once you have identified a few interesting topics, you should be reading the table of contents of journals and the abstracts of most articles in that subject area. Papers that are directly related to your research you should read in their entirety.

TIP Keep an eye out for  Review papers and special issues in your chosen subject area as they are very helpful in discovering new areas and hot topics.

TIP: you can sign up to receive table of contents or notifications when articles are published in your field from most journals or publishers.

TIP: Joining a journal club is a great way to read and dissect published papers in and around your subject area. Usually consisting of 5-10 people from the same research group or institute they meet to evaluate the good and bad points of the research presented in the paper. This not only helps you keep up to date with the field but helps you become familiar with what is necessary for a good paper which can help when you come to write your own.

If possible, communicate with some of the authors of these manuscripts via email or in person. Going to conferences if possible is a great way to meet some of these authors. Often,  talking with the author  of an important work in your research area will give you more ideas than just reading the manuscript would.

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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.

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.

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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.

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.

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 ...

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38 Comments

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.

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

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.

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.

Thanks so much. This was really helpful.

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

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

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!

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.

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

This is quite helpful. I really appreciate!

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

Very innovative and educational in approach to conducting research.

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.

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

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

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?

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?

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.

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

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

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

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

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?

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

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.

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.

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).

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

Well explained

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.

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

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

I found this article helpful. Thanks for sharing this.

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1. INTRODUCTION

2. background, 5. discussion, 6. conclusions, author contributions, competing interests, funding information, data availability, how common are explicit research questions in journal articles.

Handling Editor: Ludo Waltman

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Mike Thelwall , Amalia Mas-Bleda; How common are explicit research questions in journal articles?. Quantitative Science Studies 2020; 1 (2): 730–748. doi: https://doi.org/10.1162/qss_a_00041

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Although explicitly labeled research questions seem to be central to some fields, others do not need them. This may confuse authors, editors, readers, and reviewers of multidisciplinary research. This article assesses the extent to which research questions are explicitly mentioned in 17 out of 22 areas of scholarship from 2000 to 2018 by searching over a million full-text open access journal articles. Research questions were almost never explicitly mentioned (under 2%) by articles in engineering and physical, life, and medical sciences, and were the exception (always under 20%) for the broad fields in which they were least rare: computing, philosophy, theology, and social sciences. Nevertheless, research questions were increasingly mentioned explicitly in all fields investigated, despite a rate of 1.8% overall (1.1% after correcting for irrelevant matches). Other terminology for an article’s purpose may be more widely used instead, including aims, objectives, goals, hypotheses, and purposes, although no terminology occurs in a majority of articles in any broad field tested. Authors, editors, readers, and reviewers should therefore be aware that the use of explicitly labeled research questions or other explicit research purpose terminology is nonstandard in most or all broad fields, although it is becoming less rare.

Academic research is increasingly multidisciplinary, partly due to team research addressing practical problems. There are also now large multidisciplinary journals, such as PLOS ONE and Nature Scientific Reports , with editorial teams that manage papers written by people from diverse disciplinary backgrounds. There is therefore an increasing need for researchers to understand disciplinary norms in writing styles and paradigms. The authors of a research paper need to know how to frame its central contribution so that it is understood by multidisciplinary audiences. One strategy for this is to base an article around a set of explicitly named research questions that address gaps in prior research. Employing the standard phrase “research question” gives an unambiguous signpost for the purpose of an article and may therefore aid clarity. Other strategies include stating hypotheses, goals, or aims, or describing an objective without calling it an objective (e.g., “this paper investigates X”). Similarly, structured abstracts are believed to help readers understand a paper ( Hartley, 2004 ), perhaps partly by having an explicit aim, objective, or goal section. A paper that does not recognize or value the way in which the central contribution is conveyed may be rejected by a reviewer or editor if they are unfamiliar with the norms of the submitting field. It would therefore be helpful for authors, reviewers, and editors to know which research fields employ explicitly labeled research questions or alternative standard terminology.

Purpose statements and research questions or hypotheses are interrelated elements of the research process. Research questions are interrogative statements that reflect the problem to be addressed, usually shaped by the goal or objectives of the study ( Onwuegbuzie & Leech, 2006 ). For example, a healthcare article argued that “a good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper” and “the key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community” ( Perneger & Hudelson, 2004 ).

The choice of terminology to describe an article’s purpose seems to be conceptually arbitrary, with the final decision based on community norms, journal guidelines, and author style. For example, a research paper investigating issue X could phrase its purpose in the following ways: “research question 1: is X true?,” “this paper aims to investigate X,” “the aim/objective/purpose/goal is to investigate X,” or “X?” (as in the current paper). Implicit purpose statements might include “this paper investigates X” or just “X,” where the context makes clear that this is the purpose. Alternatively, the reader might deduce the purpose of a paper after reading it, with all these options achieving the same result with different linguistic strategies. Some research purposes might not be easily expressible as a research question, however. For example, a humanities paper might primarily discuss an issue (e.g., “Aspects of the monastery and monastic life in Adomnán’s Life of Columba ”) but even these could perhaps be expressed as research questions, if necessary (e.g., “Which are the most noteworthy aspects of the monastery and monastic life in Adomnán’s Life of Columba ?”).

In which fields are explicitly named research questions commonly used?

Has the use of explicitly named research questions increased over time?

Are research purposes addressed using alternative language in different fields?

Do large journals guide authors to use explicitly named research questions or other terminology for purpose statements in different fields?

2.1. Advice for Authors

There are some influential guidelines for reporting academic research. In the social sciences, Swales’ (1990 , 2004) Create A Research Space (CARS) model structures research article introductions in three moves (establishing a territory, establishing a niche, and occupying a niche), which are subdivided into steps. Within the 1990 model, move 3 includes the steps “outlining purposes” and “announcing present research,” but research questions are not explicitly included, being similar the “question raising” step in move 2. In the updated 2004 model, move 3 includes an obligatory step named “announcing present research descriptively and/or purposively” (that joins the steps “outlining purposes” and “announcing present research” from the 1990 model), whereas “listing research questions or hypotheses” is a new optional step.

In medicine, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative is a checklist of items that should be included to improve reporting quality. One of these is a statement of objectives that “may be formulated as specific hypotheses or as questions that the study was designed to address” or may be less precise in early studies ( Vandenbroucke, von Elm, et al., 2014 ). This description therefore includes stating research questions as one of a range of ways of specifying objectives. An informal advice article in medicine instead starts by arguing that the paper’s aim should be clearly defined ( McIntyrei, Nisbet, et al., 2007 ).

Researchers may also be guided about the language to use in papers by any ethical or other procedures that they need to follow before conducting their work. For example, clinical trials often need to be registered and declared in a standard format, which may include explicit descriptions of objectives (e.g., see “E.2.1: Main objective of the trial” at: https://www.clinicaltrialsregister.eu/ctr-search/trial/2015-002555-10/GB ).

2.2. Empirical Evidence

Journal article research questions and other purpose statements, such as aims, objectives, goals, and hypotheses ( Shehzad, 2011 ), are usually included within Introduction sections or introductory phases, sometimes appearing as separate sections ( Kwan, 2017 ; Yang & Allison, 2004 ). Some studies have analyzed research article introductions in different disciplines and languages based on the Swales’ (1990 , 2004) CARS model. Although these studies analyze small sets of articles, they seem to agree that the research article introduction structure varies across disciplines (e.g., Joseph, Lim & Nor, 2014 ) and subdisciplines within a discipline, including for engineering ( Kanoksilapatham, 2012 ; Maswana, Kanamaru, & Tajino, 2015 ), applied linguistics ( Jalilifar, 2010 ; Ozturk, 2007 ) and environmental sciences ( Samraj, 2002 ). Introductions in English seem to follow this pattern more closely than introductions in other languages ( Ahamad & Yusof, 2012 ; Hirano, 2009 ; Loi & Evans, 2010 ; Rahimi & Farnia, 2017 ; Sheldon, 2011 ), reflecting cultural differences. Research questions and other purpose terminology, such as aims, objectives, goals, or hypotheses, might also reappear within the Results or Discussion sections ( Amunai & Wannaruk, 2013 ; Brett, 1994 ; Hopkins & Dudley-Evans, 1988 ; Kanoksilapatham, 2005 ).

Previous research has shown that research questions and hypotheses are more common among English-language papers than non-English papers ( Loi & Evans, 2010 ; Mur Dueñas, 2010 ; Omidi & Farnia, 2016 ; Rahimi & Farnia, 2017 ; Sheldon, 2011 ), especially those written by English native speakers ( Sheldon, 2011 ). However, a study analyzing 119 English research article introductions from Iranian and international journals in three subdisciplines within applied linguistics found that “announcing present research” was more used in international journals whereas research questions were proclaimed explicitly more often in local journals ( Jalilifar, 2010 ).

In some fields the verbs examine , determine , evaluate , assess , and investigate are associated with the research purpose ( Cortés, 2013 ; Jalali & Moini, 2014 ; Kanoksilapatham, 2005 ) and the verbs expect , anticipate , and estimate are associated with hypotheses ( Williams, 1999 ). Some computer scientists seem to prefer to write the details of the method(s) used rather than stating the purpose or describing the nature of their research and use assumptions or research questions rather than hypotheses ( Shehzad, 2011 ). Moreover, scholars might state the hypotheses in other ways, such as “it was hypothesized that” ( Jalali & Moini, 2014 ).

A study analyzing lexical bundles (usually phrases) in medical research article introductions showed that the most frequent four-word phrases are related to the research objective, such as “the aim of the,” “aim of the present,” and “study was to evaluate” ( Jalali & Moini, 2014 ). Another study examined lexical bundles in a million-word corpus of research article introductions from several disciplines, showing that the main bundle used to announce the research descriptively and/or purposefully included the terms aim , objective , and purpose (e.g., “the aim of this paper,” “the objective of this study,” “the purpose of this paper”), but no bundles related to research questions or hypotheses were identified ( Cortés, 2013 ).

These findings are in line with other previous studies investigating the structure of research articles, especially the introduction section, which report a much higher percentage of journal papers specifying the research purpose than the research questions or hypotheses across disciplines, regardless of the language in which they are published, with the exception of law articles (see Table 1 ). These studies also show that research questions and hypotheses are much more frequent among social sciences articles (see Table 1 ), which has also been found in other genres, such as PhD theses and Master’s theses (see Table 2 ).

Reference to a wide research purposes, without specifying if they are objectives or RQs/hypotheses.

Restating RQs in the result section.

Note: Studies that have based their analysis on the Swales’s (1990) CARS model ( Anthony, 1999 ; Posteguillo, 1999 ; Mahzari & Maftoon, 2007 ) report the percentage related to “outlining purposes” and “announcing present research.” For these studies, the column “Present the research purpose” reports the higher value. Moreover, for these studies, the value reported in the RQs/hypotheses column refers to the “Question raising” information.

A few studies have focused exclusively on research purposes, research questions, and hypotheses. Some have discussed the development of research questions in qualitative ( Agee, 2009 ) or mixed method ( Onwuegbuzie & Leech, 2006 ) studies, whereas others have examined the ways of constructing research questions or hypotheses within some fields, such as organization studies ( Sandberg & Alvesson, 2011 ) or applied linguistics doctoral dissertations ( Lim, 2014 ; Lim, Loi, & Hashim, 2014 ). Shehzad (2011) examined the strategies and styles employed by computer scientists outlining purposes and listing research questions. She found an increase in the use of research nature or purpose statements and suggested that the “listing research questions or hypotheses” step of Swales’s model was obligatory in computing. No study seems to have examined how often journal guidelines give authors explicit advice about research questions or other purpose statements, however.

The PMC (Pub Med Central) Open Access subset ( www.ncbi.nlm.nih.gov/pmc/tools/openftlist/ ) was downloaded in XML format in November 2018. This is a collection of documents from open access journals or open access articles within hybrid journals. The collection has a biomedical focus, but includes at least a few articles from all broad disciplinary areas. Although a biased subset is not ideal, this is apparently the largest open access collection. Only documents declared in their XML to be of type “research article” were retained for analysis. This excludes many short contributions, such as editorials, that would not need research goals.

The XML of the body section of each article was searched for the test strings “research question,” “RESEARCH QUESTION,” “Research Question,” or “Research question,” recording whether each article contained at least one. This would miss papers exclusively using abbreviations, such as RQ1.

Full body text searches are problematic because terms could be mentioned in other contexts, depending on the part of an article. For example, the phrase “research question” in a literature review section may refer to an article reviewed. For a science-wide analysis it is not possible to be prescriptive about the sections in which a term must occur, however, because there is little uniformity in section names or orders ( Thelwall, 2019 ). Making simplifying assumptions about the position in a text in which a term should appear, such as that a research question should be stated in the first part of an article, would also not be defensible. This is because the structure of articles varies widely between journals and fields. For example, methods can appear at the end rather than the middle, and some papers start with results, with little introduction. There are also international cultural differences in the order in which sections are presented in some fields ( Teufel, 1999 ). The current paper therefore uses full-text searches without any heuristics to restrict the results for transparency and to give an almost certain upper bound to the prevalence of terms, given the lack of a high-quality alternative.

Articles were separated into broad fields using the Science-Metrics public journal classification scheme ( Archambault, Beauchesne, & Caruso, 2011 ), which allocates each journal into exactly one category. This seems to be more precise than the Scopus or Web of Science schemes ( Klavans & Boyack, 2017 ). The Science-Metrics classification was extended by adding the largest 100 journals in the PMC collection that had not been included in the original Science-Metrics classification scheme. These were classified into a Science-Metrics category by first author based on their similarity to other journals in the Science-Metrics scheme.

Five of the broad fields had too little data to be useful (Economics & Business; Visual & Performing Arts; Communication & Text Studies; General Arts, Humanities & Social Sciences; Built Environment & Design) and were removed. Years before 2000 were not included because of their age and small amount of data. Individual field/year combinations were also removed when there were fewer than 30 articles, since they might give a misleading percentage. Each of the 17 remaining categories contained at least 630 articles ( Table 3 ), with exact numbers for each field and year available in the online supplementary material (columns AE to AW: https://doi.org/10.6084/m9.figshare.10274012 ). For all broad fields, most articles have been published in the last 5 years (2014–2018), with the exception of Historical Studies, Chemistry, and Enabling & Strategic Technology.

For the third research question, alternative terms for research goals were searched for in the full text of articles. These terms might all be used in different contexts, so a match is not necessarily related to the main goal of the paper (e.g., the term “question” could be part of a discussion of a questionnaire), but the rank order between disciplines may be informative and the results serve as an upper bound for valid uses. The terms searched for were “research questions,” “questions,” “hypotheses,” “aims,” “objectives,” “goals,” and “purposes” in both singular and plural forms. These have been identified above as performing similar functions in research. For this exploration, the term “question” is used in addition to “research question” to capture more general uses.

Any of the queried terms could be included in an article out of context. For example, “research question” could be mentioned in a literature review rather than to describe the purpose of the new article. To check the context in which each term was used, a random sample of 100 articles (using a random number generator) matching each term (200 for each concept, counting both singular and plural, totaling 1,400 checks) was manually examined to ascertain whether any use of the term in the article stated the purpose of the paper directly (e.g., “Our research questions were…”) or indirectly (e.g., “This answered our research questions”), unless mentioned peripherally as information to others (e.g., “The study research questions were explained to interviewees”). There did not seem to be stock phrases that could be used to eliminate a substantial proportion of the irrelevant matches (e.g., “objective function” or “microscope objective”). There also was not a set of standard phrases that collectively could unambiguously identify the vast majority of research questions (e.g., “Our research questions were” or “This article’s research question is”).

Journal guidelines given to authors were manually analyzed to check whether they give advice about research questions and other purpose statements. Three journals with the most articles in each of the 17 academic fields were selected for this (see online supplement doi.org/10.6084/m9.figshare.10274012 ). This information is useful background context to help interpret the results.

4.1. RQ1 and RQ2: Articles Mentioning Research Questions

Altogether, 23,282 out of 1,314,412 articles explicitly mentioned the phrases “research question” or “research questions” (1.8%), although no field included them in more than a fifth of articles in recent years and there are substantial differences between broad fields ( Figure 1 ). When the terms are used in an article they usually (63%, from the 1,400 manual checks) refer to the article’s main research question(s). Other uses of these terms include referring to questions raised by the findings, and a discussion of other articles’ research questions in literature review sections or as part of the selection criteria of meta-analyses. Thus, overall, only 1.1% of PMC full-text research articles mention their research questions explicitly using the singular or plural form. There has been a general trend for the increasing use of these terms, however ( Figure 2 ).

The percentage of full-text research articles containing the phrases “research question” or “research questions” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 63% of occurrences of these terms described the hosting article’s research question(s) ( n = 801,895 research articles).

As for Figure 1 but covering 2000–2018 ( n = 1,314,412 research articles). (All fields can be identified in the Excel versions of the graph within the online supplement 10.6084/m9.figshare.10274012).

If the terms “question” or “questions” are searched for instead, there are many more matches, although for a minority of articles in most fields ( Figures 3 and 4 ). When these terms are mentioned, they rarely (17%) refer to the hosting article’s research questions (excluding matches with the exact phrases “research question” or “research questions” to avoid overlaps with the previous figure). Common other contexts for these terms include questions in questionnaires and questions raised by the findings. Sometimes the term “question” occurred within an idiomatic phrase or issue rather than a query (e.g., “considerable temperature gradients occur within the materials in question” and “these effects may vary for different medications. Future studies are needed to address this important question”). In Philosophy & Theology, the matches could be for discussions of various questions within an article, rather than a research question that is an article’s focus. Similarly for Social Sciences and Public Health & Health Services, the question mentioned might be in questionnaires rather than being a research question. After correcting for the global irrelevant matches, which is a rough approximation, in all broad fields fewer than 14% of research articles use these terms to refer to research questions. Nevertheless, this implies that the terms “question” or “questions” are used much more often than the phrases “research question” or “research questions” (1.8%) to refer to an article’s research purposes.

The percentage of full-text research articles containing the terms “question” or “questions” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 17% of occurrences of these terms described the hosting article’s main research question(s) without using the exact phrases “research question” or “research questions,” not overlapping with Figure 1(a) ( n = 801,895 research articles).

As for Figure 3 , but covering 2000–2018 ( n = 1,314,412 research articles).

4.2. RQ3: Other Article Purpose Terms

The terms “hypothesis” and “hypotheses” are common in Psychology and Cognitive Science as well as in Biology ( Figure 5 ). They are used in a minority of articles in all other fields, but, by 2018 were used in at least 15% of all (or 4% after correcting for irrelevant matches). The terms can be used to discuss statistical results from other papers and in philosophy and mathematics they can be used to frame arguments, so not all matches relate to an article’s main purpose, and only 28% of the random sample checked used the terms to refer to the articles’ main hypothesis or hypotheses.

The percentage of full-text research articles containing the terms “hypothesis” or “hypotheses” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 28% of occurrences of these terms described the hosting article’s main hypothesis or hypotheses. A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

The use of the terms “aim” and “aims” is increasing overall, possibly in all academic fields ( Figures 6 and 7 ). Fields frequently using the term include Philosophy & Theology, Information & Communication Technologies (ICTs) and Public Health & Health Services, whereas it is used in only about 20% of Chemistry and Biomedical Research papers. Articles using the terms mostly use them (especially the singular “aim”) to describe their main aim (70%), so these are the terms most commonly used to describe the purpose of a PMC full-text article. The terms are also sometimes used to refer to wider project aims or relevant aims outside of the project (e.g., “The EU’s biodiversity protection strategy aims to preserve…”).

The percentage of full-text research articles containing the terms “aim” or “aims” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 70% of occurrences of these terms described the hosting article’s main aim(s) ( n = 801,895 research articles).

As for Figure 6 , but covering 2000–2018 ( n = 1,314,412 research articles).

The terms “objective” and “objectives” are reasonably common in most academic fields ( Figure 8 ) and are used half of the time (52%) for the hosting article’s objectives. Other common uses include lenses and as an antonym of subjective (e.g., “high-frequency ultrasound allows an objective assessment…”). It is again popular within ICTs, Philosophy & Theology, and Public Health & Health Services, whereas it is used in only about 12% of Physics & Astronomy articles.

The percentage of full-text research articles containing the terms “objective” or “objectives” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 52% of occurrences of these terms described the hosting article’s objective(s). A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

The terms “goal” and “goals” follow a similar pattern to “aim” and “objective” ( Figure 9 ), but refer to the hosting paper’s goals in only 28% of cases. Common other uses include methods goals (“the overall goal of this protocol is…”) and field-wide goals (e.g., “over the last decades, attempts to integrate ecological and evolutionary dynamics have been the goal of many studies”).

The percentage of full-text research articles containing the terms “goal” or “goals” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 28% of occurrences of these terms described the hosting article’s research question(s). A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

Some articles may also use the terms “purpose” or “purposes” rather than the arguably more specific terms investigated above, and there are disciplinary differences in the extent to which they are used ( Figure 10 ). These terms may also be employed to explain or justify aspects of an article’s methods. When used, they referred to main purposes in fewer than a third of articles (29%), and were often instead used to discuss methods details (e.g., “it was decided a priori that physical examination measures would not be collected for the purpose of this audit”), background information (e.g., “species are harvested through fishing or hunting, mainly for alimentary purposes”) or ethics (e.g., “Animal care was carried out in compliance with Korean regulations regarding the protection of animals used for experimental and other scientific purposes.”).

The percentage of full-text research articles containing the terms “purpose” or “purposes” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 29% of occurrences of these terms described the hosting article’s purpose(s). A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

4.3. RQ4: Journal Guidelines

“The motivation or purpose of your research should appear in the Introduction, where you state the questions you sought to answer” ( zookeys.pensoft.net/about )

“Define the purpose of the work and its significance, including specific hypotheses being tested” ( www.mdpi.com/journal/nutrients/instructions )

“The introduction briefly justifies the research and specifies the hypotheses to be tested” ( www.ajas.info/authors/authors.php )

“A brief outline of the question the study attempts to address” ( onlinelibrary.wiley.com/page/journal/20457758/homepage/registeredreports.html )

“Acquaint the reader with the findings of others in the field and with the problem or question that the investigation addresses.” ( www.oncotarget.com )

“State the research objective of the study, or hypothesis tested” ( www.springer.com/biomed/human+physiology/journal/11517 )

In the first quote above, for example, “state the questions” could be addressed literally by listing (research) questions or less literally by stating the research objectives. Thus, journal guidelines seem to leave authors the flexibility to choose how to state their research purpose, even if suggesting that research questions or hypotheses are used. This also applies to the influential American Psychological Society guidelines, such as, “In empirical studies, [explaining your approach to solving the problem] usually involves stating your hypotheses or specific question” ( APA, 2009 , p. 28).

An important limitation of the methods is that the sample contains a small and biased subset of all open access research articles. For example, the open access publishers BMC, Hindawi, and MDPI have large journals in the data set. The small fields ( Table 3 ) can have unstable lines in the graphs because of a lack of data. Sharp changes between years for the same field are likely due to either small amounts of data or changes in the journals submitted to PubMed in those years, rather than changes in field norms. It is possible that the proportions discovered would be different for other collections. Another limitation is that although articles were searched with the text string “research question,” this may not always have signified research questions in the articles processed (e.g., if mentioned in a literature review or in a phrase such as “this research questions whether”). Although the corrections reported address this, they provide global correction figures rather than field-specific corrections. Conversely, a research question may just be described as a question (e.g., “the query of this research”) or phrased as a question without describing it as such (e.g., “To discover whether PGA implants are immunologically inert…”). Thus, the field-level results are only indicative.

RQ1: Only 23,282 (1.8%, 1.1% after correcting for irrelevant matches) out of 1,314,412 articles assessed in the current paper explicitly mentioned “research question(s),” with significant differences between fields. Although there has been a general trend for the increasing use of explicitly named research questions, they were employed in fewer than a quarter of articles in all fields. Research questions were mostly used by articles in Social Sciences, Philosophy & Theology, and ICTs, whereas they have been mentioned by under 2% of articles in engineering, physical, life, and medical sciences. Previous studies have shown that 73.3% of English articles in Physical Education ( Omidi & Farnia, 2016 ), 33% of Applied Linguistics articles ( Sheldon, 2011 ) and 32% of Computer Science articles ( Shehzad, 2011 ) included research questions or hypotheses. Studies focused on doctoral dissertations show that 97% of U.S. Applied Linguistics ( Lim, 2014 ), 90% of English Language Teaching ( Geçíklí, 2013 ), 70% of Education Management ( Cheung, 2012 ), and 50% of computing doctoral dissertations ( Soler-Monreal, Carbonell-Olivares, & Gil-Salom, 2011 ) listed research questions, a large difference.

The results also show that about 13% of Public Health and Health Services articles and 12% of Psychology and Cognitive Science articles use the term “research questions.” However, a study focused on Educational Psychology found that 35% of English-language papers listed research questions and 75% listed hypotheses ( Loi & Evans, 2010 ). Thus, the current results reveal a substantially lower overall prevalence than suggested by previous research.

RQ2: There has been a substantial increase in the use of the term “research questions” in some subjects, including ICTs, Social Sciences, and Public Health and Health Services ( Figure 2 ), as well as a general trend for increasing use of this term, but with most fields still rarely using it. This suggests that some disciplines are standardizing their terminology, either through author guidelines in journals (RQ4), formal training aided by frameworks such as Swales’ CARS model, or informal training or imitation. For example, the analysis of the “instructions for authors” given by 51 journals (online supplement doi.org/10.6084/m9.figshare.10274012 ) showed that the three biology journals, the three psychology journals, and two biomedical journals included in the analysis referred to both research questions and hypotheses in their author guidelines.

RQ3: Terminology for the purpose of an article seems to be quite widely used, including aims, objectives, and goals ( Figures 5 – 9 ). This is in line with a study examining the lexical bundles identified in research article introductions from several disciplines, which reported the terms “aim,” “objective,” and “purpose” as the main terms used to announce the research descriptively and/or purposefully, although no phrase related to research questions or hypotheses was identified ( Cortés, 2013 ), and with another study reporting similar terminology in medical articles ( Jalali & Moini, 2014 ). Related to this (RQ4), the analysis of the “instructions for authors” given by 51 journals (online supplement 10.6084/m9.figshare.10274012) showed that “purpose” is the term mostly mentioned in the Abstract guidelines and “aims” is the term mainly used in the body of the text (Introduction or Background) guidelines. The term “objective” also appears in some article body guidelines, whereas the term “goal” is not mentioned in them. After correcting for irrelevant matches (e.g., articles using the term “hypothesis” but not for their main research hypotheses) using the percentages reported with the figures above, no terminology was found in a majority of articles in any field. Thus, at least from the perspective of PMC Open Access publications, there is no standardization of research terminology in any broad field.

There are substantial disciplinary differences in the terminology used. Whereas the term “research question” is relevant in Social Sciences, Philosophy & Theology, and ICTs, the term “hypothesis” is important in Psychology and Cognitive Science, used in over 60% of articles. This is in line with a study focused on Educational Psychology, which found that the 75% out of 20 English papers introduced the hypotheses, whereas 35% of them introduced the research questions ( Loi & Evans, 2010 ). The three psychology journals with the highest frequency in the data set used for this study referred to hypotheses in their author guidelines (see online supplement 10.6084/m9.figshare.10274012).

The terms “aim,” “objective,” and “goal” are mainly used in Philosophy, Theology, ICTs, and Health. The term “aim” is also quite often used in health, mathematics, and psychological articles, whereas the term “objective” is also used in engineering and mathematics articles. The term “goal” is also used in psychology and biomedical articles. Although most articles in all fields include a term that could be used to specify the purpose of an article (question or questions, hypothesis, aim, objective, goal), they are relatively scarce in Chemistry and Physics & Astronomy. The use of purpose-related terms has also increased over time in most academic fields. This agrees with a study about Computer Science research articles that found an increasing use of outlining purpose or stating the nature of the research ( Shehzad, 2011 ).

An example article from Chemistry illustrates how a research purpose can be implicit. The paper, “Fluid catalytic cracking in a rotating fluidized bed in a static geometry: a CFD analysis accounting for the distribution of the catalyst coke content” has a purpose that is clear from its title but that is not described explicitly in the text. Its abstract starts by describing what the paper offers, but not why, “Computational Fluid Dynamics is used to evaluate the use of a rotating fluidized bed in a static geometry for the catalytic cracking of gas oil.” The first sentence of the last paragraph of the introduction performs a similar role, “The current paper presents CFD simulations of FCC in a RFB-SG using a model that accounts for a possible nonuniform temperature and catalyst coke content distribution in the reactor.” Both sentences could easily be rephrased to start with, “The purpose of this paper is to,” but it is apparently a stylistic feature of chemical research not to do this. Presumably purposes are clear enough in typical chemistry research that they do not need to be flagged linguistically, but this is untrue for much social science and health research, for example, partly due to nonstandard goals (i.e., task uncertainty: Whitley, 2000 ).

5.1. Possible Origins of the Differences Found

Broad epistemological: Fields work with knowledge in different ways and naturally use different terminology as a result. Arts and humanities research may have the goal to critique or analyze, or may be practice-based research rather than having a more specific knowledge purpose. For this, research questions would be inappropriate. Thus, terminology variation may partly reflect the extent to which a broad field typically attempts to create knowledge.

Narrow epistemological: Narrow fields that address similar problems may feel that they do not need to use research problem terminology to describe their work because the purpose of a paper is usually transparent from the description of the methods or outcome. For example, it would be unnecessary to formulate, “This paper investigates whether treatment x reduces death rates from disease y” as a named research question or even explain that it is the goal of a paper. This may also be relevant for fields that write short papers. It may be most relevant for papers that use statistical methods and have high standards of evidence requirement (e.g., medicine) and clearly defined problems. In contrast, many social sciences research projects are not intrinsically clearly demarcated and need an explanation to define the problem (as for the current article). Thus, describing what the problem is can be an important and nontrivial part of the research. This relates to “task uncertainty,” which varies substantially between fields ( Whitley, 2000 ) and affects scholarly communication ( Fry, 2006 ).

Field or audience homogeneity: Fields with homogeneous levels and types of expertise may avoid terminology that field members would be able to deduce from the context. For example, a mixed audience paper might need to specify statistical hypotheses, whereas a narrow audience paper might only need to specify the result, because the audience would understand the implicit null and alternative hypotheses.

Field cultures for term choice: Academic publishing relies to some extent on imitation and reaching a consensus about the ways in which research is presented (e.g., Becher & Trowler, 2001 ). It might therefore become a field norm to use one term in preference to a range of synonyms, such as “aims” instead of “objectives.”

Field cultures for term meaning: Following from the above, a field culture may evolve an informal convention that two synonyms have different specific uses. For example, “aims” could be used for wider goals and “objectives” for the narrower goals of a paper.

Guidelines: Fields or their core journals may adopt guidelines that specify terminology, presumably because they believe that this standardization will improve overall communication clarity.

The results suggest that the explicit use of research questions, in the sense that they are named as such, is almost completely absent in some research fields, and they are at best a substantial minority (under 20%) in most others (ignoring the fields that did not meet the inclusion threshold). Although the word search approach does not give conclusive findings, the results suggest that alternative terminologies for describing the purpose of a paper are more widespread in some fields, but no single terminology is used to describe research purposes in a majority of articles in any of the broad fields examined.

The lack of standardization for purpose terminology in most or all fields may cause problems for reviewers and readers expecting to see explicit statements. It is not clear whether guidelines to standardize terminology for journals or fields would be practical or helpful, however, but this should be explored in the future. Presumably any guidelines should allow exceptions for articles that make nonstandard contributions, although there are already successful journals with prescriptive guidelines, and the advantage of standardization through structured abstracts seems to be accepted ( Hartley, 2004 ).

The disciplinary differences found may cause problems for referees, authors, editors, and readers of interdisciplinary research or research from outside of their natural field if they fail to find an article’s purpose expressed in the terminology that they expect. This issue could not reasonably be resolved by standardizing across science because of the differing nature of research. Instead, evidence in the current article of the existence of valid disciplinary differences in style may help reviewers and editors of large interdisciplinary journals to accept stylistic differences in research problem formulations.

Mike Thelwall: Conceptualization, Investigation, Software, Writing—original draft. Amalia Mas-Bleda: Investigation, Writing—original draft.

The authors have no competing interests to declare.

This research received no funding.

The data behind the results are available at FigShare ( https://doi.org/10.6084/m9.figshare.10274012 ).

Author notes

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The Art of Asking Smarter Questions

• Arnaud Chevallier,
• Frédéric Dalsace,
• Jean-Louis Barsoux

With organizations of all sorts facing increased urgency and unpredictability, being able to ask smart questions has become key. But unlike lawyers, doctors, and psychologists, business professionals are not formally trained on what kinds of questions to ask when approaching a problem. They must learn as they go. In their research and consulting, the authors have seen that certain kinds of questions have gained resonance across the business world. In a three-year project they asked executives to brainstorm about the decisions they’ve faced and the kinds of inquiry they’ve pursued. In this article they share what they’ve learned and offer a practical framework for the five types of questions to ask during strategic decision-making: investigative, speculative, productive, interpretive, and subjective. By attending to each, leaders and teams can become more likely to cover all the areas that need to be explored, and they’ll surface information and options they might otherwise have missed.

These five techniques can drive great strategic decision-making.

Idea in Brief

The situation.

With organizations of all sorts facing increased urgency and uncertainty, the ability to ask smart questions has become key. But business professionals aren’t formally trained in that skill.

Why It’s So Challenging

Managers’ expertise often blinds them to new ideas. And the flow of questions can be hard to process in real time, so certain concerns and insights may never be raised.

Strategic questions can be grouped into five domains: investigative, speculative, productive, interpretive, and subjective. By attending to each, leaders and teams are more likely to cover all the areas that need to be explored—and they’ll surface information and options they might otherwise have missed.

As a cofounder and the CEO of the U.S. chipmaker Nvidia, Jensen Huang operates in a high-velocity industry requiring agile, innovative thinking. Reflecting on how his leadership style has evolved, he told the New York Times, “I probably give fewer answers and I ask a lot more questions….It’s almost possible now for me to go through a day and do nothing but ask questions.” He continued, “Through probing, I help [my management team]…explore ideas that they didn’t realize needed to be explored.”

• Arnaud Chevallier is a professor of strategy at IMD Business School.
• Frédéric Dalsace is a professor of marketing and strategy at IMD.
• Jean-Louis Barsoux is a term research professor at IMD and a coauthor of ALIEN Thinking: The Unconventional Path to Breakthrough Ideas (PublicAffairs, 2021).

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Grizzly bear conservation is as much about human relationships as it is the animals

Associate Professor of Human Dimensions of Natural Resources, University of Montana

Disclosure statement

Alexander L. Metcalf has received funding from the National Fish and Wildlife Foundation, the National Science Foundation, the Richard King Mellon Foundation, the Pennsylvania Department of Conservation and Natural Resources, the Montana Department of Fish, Wildlife and Parks, the US Geological Survey, and the US Department of Agriculture Forest Service. Dr. Metcalf is an advisor to the Swan Valley Connections board of directors.

University of Montana provides funding as a member of The Conversation US.

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Montanans know spring has officially arrived when grizzly bears emerge from their dens . But unlike the bears, the contentious debate over their future never hibernates. New research from my lab reveals how people’s social identities and the dynamics between social groups may play a larger role in these debates than even the animals themselves.

Social scientists like me work to understand the human dimensions behind wildlife conservation and management. There’s a cliché among wildlife biologists that wildlife management is really people management, and they’re right. My research seeks to understand the psychological and social factors that underlie pressing environmental challenges. It is from this perspective that my team sought to understand how Montanans think about grizzly bears.

To list or delist, that is the question

In 1975, the grizzly bear was listed as threatened under the Endangered Species Act following decades of extermination efforts and habitat loss that severely constrained their range . At that time, there were 700-800 grizzly bears in the lower 48 states, down from a historic 50,000 . Today, there are about 2,000 grizzly bears in this area, and sometime in 2024 the U.S. Fish and Wildlife Service will decide whether to maintain their protected status or begin the delisting process.

Listed species are managed by the federal government until they have recovered and management responsibility can return to the states. While listed, federal law prevents hunting of the animal and destruction of grizzly bear habitat. If the animal is delisted, some states intend to implement a grizzly bear hunting season .

People on both sides of the delisting debate often use logic to try to convince others that their position is right. Proponents of delisting say that hunting grizzly bears can help reduce conflict between grizzly bears and humans . Opponents of delisting counter that state agencies cannot be trusted to responsibly manage grizzly bears.

But debates over wildlife might be more complex than these arguments imply.

Identity over facts

Humans have survived because of our evolved ability to cooperate . As a result, human brains are hardwired to favor people who are part of their social groups , even when those groups are randomly assigned and the group members are anonymous .

Humans perceive reality through the lens of their social identities. People are more likely to see a foul committed by a rival sports team than one committed by the team they’re rooting for. When randomly assigned to be part of a group, people will even overlook subconscious racial biases to favor their fellow group members.

Leaders can leverage social identities to inspire cooperation and collective action . For example, during the COVID-19 pandemic, people with strong national identities were more likely to physically distance and support public health policies.

But the forces of social identity have a dark side, too. For example, when people think that another “out-group” is threatening their group, they tend to assume members of the other group hold more extreme positions than they really do . Polarization between groups can worsen when people convince themselves that their group’s positions are inherently right and the other group’s are wrong. In extreme instances, group members can use these beliefs to justify immoral treatment of out-group members .

Empathy reserved for in-group members

These group dynamics help explain people’s attitudes toward grizzly bears in Montana . Although property damage from grizzly bears is extremely rare, affecting far less than 1% of Montanans each year , grizzly bears have been known to break into garages to access food , prey on free-range livestock and sometimes even maul or kill people .

People who hunt tend to have more negative experiences with grizzly bears than nonhunters – usually because hunters are more often living near and moving through grizzly bear habitat.

In a large survey of Montana residents, my team found that one of the most important factors associated with negative attitudes toward grizzly bears was whether someone had heard stories of grizzly bears causing other people property damage. We called this “vicarious property damage.” These negative feelings toward grizzly bears are highly correlated with the belief that there are too many grizzly bears in Montana already.

But we also found an interesting wrinkle in the data . Although hunters extended empathy to other hunters whose properties had been damaged by grizzly bears, nonhunters didn’t show the same courtesy. Because property damage from grizzly bears was far more likely to affect hunters, only other hunters were able to put themselves in their shoes. They felt as though other hunters’ experiences may as well have happened to them, and their attitudes toward grizzly bears were more negative as a result.

For nonhunters, hearing stories about grizzly bears causing damage to hunters’ property did not affect their attitudes toward the animals.

Identity-informed conservation

Recognizing that social identities can play a major role in wildlife conservation debates helps untangle and perhaps prevent some of the conflict. For those wishing to build consensus, there are many psychology-informed strategies for improving relationships between groups .

For example, conversations between members of different groups can help people realize they have shared values . Hearing about a member of your group helping a member of another group can inspire people to extend empathy to out-group members.

Conservation groups and wildlife managers should take care when developing interventions based on social identity to prevent them from backfiring when applied to wildlife conservation issues. Bringing up social identities can sometimes cause unintended division. For example, partisan politics can unnecessarily divide people on environmental issues .

Wildlife professionals can reach their audience more effectively by matching their message and messengers to the social identities of their audience . Some conservation groups have seen success uniting community members who might otherwise be divided around a shared identity associated with their love of a particular place. The conservation group Swan Valley Connections has used this strategy in Montana’s Swan Valley to reduce conflict between grizzly bears and local residents.

Group dynamics can foster cooperation or create division, and the debate over grizzly bear management in Montana is no exception. Who people are and who they care about drives their reactions to this large carnivore. Grizzly bear conservation efforts that unite people around shared identities are far more likely to succeed than those that remind them of their divisions.

• Conservation
• Environment
• Endangered Species Act
• Grizzly bears
• Social identity
• Wildlife management
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April 15, 2024

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If Alien Life Is Found, How Should Scientists Break the News?

At a recent workshop, researchers and journalists debated how to announce a potential discovery of extraterrestrial life

By Sarah Scoles

Thomas Fuchs

If one day scientists discover evidence of extraterrestrial life, how will they tell the world ? How certain will they be of their discovery, and how will the public know what sense to make of it? Will the news cause fear, existential agony, dancing in the streets or merely a worldwide shrug? And how much will that reaction depend on the news’s delivery?

During four days in February and March astrobiologists, journalists, science communicators, communications scholars, ethicists and artists got together digitally at a NASA Astrobiology Program workshop to discuss those questions. Over Zoom the participants discussed how researchers might find that elusive evidence of alien life in the universe and how to talk publicly about those hypothetical discoveries. “We all have our own disciplines,” says Jack Madden, an astrobiologist-turned-artist, who attended the workshop. “And this is a multidisciplinary endeavor. So we’re isolated in the knowledge we have and what other people are doing.” Part of the goal of the project was to cinch that knowledge gap.

The motley crew at the event, called “Communicating Discoveries in the Search for Life in the Universe,” spent their four sessions together hashing out the lessons astrobiology could take from the past and the ways they might be applied to the future—a future in which, perhaps, scientists will find evidence of extraterrestrial biology.

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But here’s the problem with the future: no one can foretell it. Will a discovery of alien life ever happen? Is there any alien life ? What forms might that life and its discovery take ? And what will the headline writers of 2028 (or 2058 or 2888) do with all that information?

No one knows answers to those questions, but because scientists love to predict, they and the other workshop participants made educated guesses and gamed them out with an eye toward relaying information about aliens to the rest of this world. As with the question of extraterrestrial life itself, though, concrete answers and plans were hard to come by.

In trying to predict the future, the past is always key—history is wont to repeat itself, at least on Earth. And so the workshop attendees discussed old tales of extraterrestrial news, such as the story of ALH84001 . Also known as the “ Allan Hills meteorite ,” this space rock traveled from Mars to land in the wilds of Antarctica, creating a dark, sharp spot among the ice fields. The meteorite had formed more than four billion years ago and then shot to space around 16 million years ago. It spent most of that time wandering the cosmic wilderness before ending up on Earth’s southernmost continent 13,000 years ago.

In 1996 a group of scientists—some of whom were affiliated with NASA— claimed in the journal Science that the special space rock, which the team had scrutinized closely by electron microscope, seemed to have microscopic fossils whose tubular, wormy character resembled that of earthly bacteria. “The image spoke immediately to a person,” Madden says—to laypeople, who know what bacteria look like, and to the scientists, who had been exposed to the same images of microorganisms for their whole life. “Having an image in that situation was so powerful,” he continues. The meteorite also had organic molecules and carbonate globules that contained magnetite, which further supported the idea that the rock might shake up the field of biology.

The findings, which were thought to potentially be the first evidence of life from another planet, shot through the headlines like the meteorite itself. Then-president Bill Clinton even gave a public speech about it after NASA had held its own press conference. “If this discovery is confirmed, it will surely be one of the most stunning insights into our universe that science has ever uncovered,” Clinton said. “Its implications are as far-reaching and awe-inspiring as can be imagined.”

In the view of most scientists, though, that confirmation hasn’t come through. Decades later the results remain the subject of debate, but the scientific consensus is that the rock’s chemistry and embedded shapes could be explained with mere geology and chemistry, not biology. During the initial announcements, officials showed caution and hedged their bets. “Like all discoveries, this one will and should continue to be reviewed, examined and scrutinized,” Clinton said.

But some scientists, including some who attended the workshop, see the hype around this preliminary and ultimately contested result as a failure nonetheless. They think that the finding’s announcers jumped the gun, prematurely waving “jazz hands” (a phrase workshop participants used to indicate official hype). This type of overpromotion can undermine trust and pass sticky but likely incorrect information into the public domain.

The kind of debate that ALH84001 started, though, is an important part of the scientific process. And scientific results are more likely to spark discussion that could lead, haltingly, toward the right answer if they are high-stakes—and maybe even a little hyped. Take, for instance, the 2020 announcement that scientists had found evidence of phosphine on Venus—and that, as far as they could tell, only life could produce that chemical on the searing, pressurized planet.

The result made splashy headlines—at the workshop, both journalists and scientists acknowledged that splashy headlines are here to stay—and it also caught the attention of scientists who scrutinized the claim and disagreed, in a debate that, like with ALH84001, is still going on. “This is how science evolves,” says Sarah Rugheimer, an astrobiologist and chair for the public understanding of astronomy at York University in Ontario and a workshop participant. “If I make a small, middling claim, no one’s going to care about it or read about it.”

Future discoveries are likely to include more like Venus’s alleged phosphine: today’s scientists, including many of those who attended the workshop, are keen to use super-powerful observatories such as the James Webb Space Telescope to search for “biosignatures” in the atmospheres of exoplanets—that is, chemicals they believe are produced by living beings.

Unlike with the Allan Hills meteorite, such data will provide nothing tangible to study, just photons and spectral fingerprints on computer screens. Proving that only living beings—and not, as with ALH84001, hypothetical quirks of geochemistry—have produced a purported biosignature is going to be difficult and maybe impossible. Scientists could send a probe to Venus to hunt for the hypothetical makers of phosphine, but any exoplanetary biosignature scientists find may be destined to be called “a sign that’s consistent with life” rather than “a sign of life” for a long time, if not forever. “I think that discovery of life is going to be an incremental process,” says Victoria Meadows, an astrobiologist at the University of Washington, who attended the workshop. “Unless something wanders past the camera and waves to us or encodes pi and transmits that in a radio signal, it’s not going to be definitive. And there’s going to be a lot of discussion going on.”

Scientists at the workshop and throughout history have worried that readers of news stories can’t hold that uncertainty or grasp the idea that follow-up research will be required. Some are not convinced that the public is scientifically literate enough to know or be shown that science is a process . But that’s perhaps an unfair assessment: often people see science as a set of settled results because that’s how it’s presented in public—in news stories and sometimes by scientists themselves. “At some level, we sort of hobble ourselves a bit in assuming that people can’t follow along, and I think that’s unfair,” Meadows says. In the workshop discussions, journalists called for agencies such as NASA to be more forthcoming, candid and timely—which would allow reporters to access the information and scientist sources they need to write about potential discoveries in a nuanced way. Many (though not all) scientists agreed, expressing frustration with the many levels of permission required to do public interviews and with statements carefully crafted by committee much too slowly for the news cycle.

Conveying where a result stands within ongoing debates while follow-up observations are happening is the key to good public information. But scientists are often reluctant to critique their peers’ work, and agencies such as NASA aren’t always keen to approve their researchers to do so. This makes it difficult, journalists at the workshop said, for the media or the public to evaluate a result’s status.

Something that could help with that is a set of “standards of evidence” for life claims—sort of like a key or legend to portray how many grains of salt to take with an alien life announcement. The standards would also show how those grains might dissolve as more evidence sluices in. “[The research process] is this arc of discovery, rather than a single point of discovery,” Meadows says. Scientists could come forward at any time during the research, including when results are far from certain, “as long as [they’re] clear about where [they] are in that process.”

Coming to the realization that the discovery of life, if it happens, will be an arc and not a point—and maybe a gray arc rather than a black-and-white one—was a process of its own for Rugheimer. “I started a little more bright-eyed and bushy-tailed way back when, and I thought we’d be able to” make a surefire discovery, she says. Now she’s taking a more practical approach. “The point when I pull out the champagne might just be what I consider a really strong signal but not something that might be 100 percent definite.”

To Adam Robinson, an astrobiologist at St. Petersburg College and NASA's Jet Propulsion Laboratory, Biodefense Coordinator at the Florida Bureau of Labs, and a workshop participant, the process of looking for signs of extraterrestrial life—and the possibility of spending years discovering and interpreting these signs—isn’t frustrating. It is, he says, inspiring.

“It’s the whole idea of people planting trees for shade that they’ll never sit under,” he says. “I went to a conference two years ago, and I was sitting there listening to these people planning these planetary science missions [in which] they’d probably be dead when the data came back. And it’s awesome to see people just commit their lives and their whole careers to something that they may never see.”

At the conclusion of the workshop, participants tried to sum up their conclusions in shared Google docs, to mixed success. If there was one takeaway, it was that attendees of all sorts thought news of potential alien discoveries shouldn’t shy away from the caveats: good public reporting should talk about uncertainty, include criticism, discuss the arduous process of confirmation and be honest about how far in the future a yes or no to the “aliens?” question is likely to be. That kind of communication demonstrates transparency and gives important context, even if a headline doesn’t.

Much of the workshop’s discussion, somewhat contradictorily, was nonetheless about how scientists and agencies such as NASA could coordinate messages that reach the public about those same discoveries. Crafting a message so tightly, though, doesn’t typically engender trust or read as transparent—something journalists pointed out in discussions. And besides, it rarely works: Plan a big, choreographed press conference for your best biosignature candidate for Tuesday at 8:14 A.M. EDT, and someone can leak the results Sunday at zero dark thirty. If you write an understated headline for your press release, be prepared for a tabloid (or digital publication hungry for traffic) to run with “NASA Finds Aliens!!!”

And then there are the inevitable mixed messages that occur when people aren’t familiar with the research process. Take the Mars Sample Return mission, which is set to launch later this decade and will bring material from the Red Planet back to Earth, in part to search for evidence of life. In one of the workshop’s scenarios, participants considered what might happen if the mission returned remnants of something alive, which would leave people concerned that Martian germs might bring a new plague to Earth.

The chance that Mars has Earth-harming microbes is extremely small—but not zero. And that’s why scientists plan to treat all material brought back as if it could be very dangerous. “They’re going to be housed in a facility that essentially is what we use to manipulate deadly pathogens such as Ebola,” Robinson says—basically a biosafety level 4 lab meant to protect people outside and in from danger. “So people hear that, and they’re going to be like, ‘I’ve got a scientist telling me there’s probably nothing to worry about. Then why are they putting it in there if there’s nothing to worry about?’”

Another communication complication, according to the Zoom meeting attendees, is that a significant proportion of the public thinks that we already have found aliens. And who can blame people? Headlines have suggested as much over and over for years for titillating false alarms or inconclusive results. If a statement includes the words “found aliens,” that’s, sensibly, the part that sticks, even if it also says “might have,” and “possibly”—especially if it meshes with a reader’s, watcher’s or listener’s existing worldview.

To nobly convey uncertainty, the nature of the scientific process and ongoing debate, then, means to first debunk incorrect information. The fact that that incorrect information is out there, though, also suggests that maybe the discovery of extraterrestrials wouldn’t actually be that Earth-shattering. After all, people who think aliens are among us have just gone on with their life.

With all those complications (and more), Rugheimer was left, at the end of the workshop, thinking that perhaps the discussion had focused too much on the wrong things—specifically, message control.

No matter how scripted a press conference is, people are going to think what they’re going to think—and people being people, they’re going to think a cornucopia of things. We have to “be real,” Rugheimer says, about how the next potential alien discovery might go down. “Someone is going to find something they think is cool. They’re going to try to publish it first. They’re going to be overinflated in what that discovery is,” she says.

“This is what happens,” she adds. “We’ve seen this time and time again. That’s how science works, too.”

In fact, the historical scenarios the workshop examined basically all occurred like this—in direct contradiction to the plans the participants were making for the future. To expect that the future will somehow unfold differently while humans as a species remain the same is magical thinking, Rugheimer says. “I think we as a community need to come to peace with the fact that there’s only so much we can do to try to be careful,” she continues. “Because as soon as there’s an exciting discovery, no one is going to be careful. And that’s okay.”

Sure, she continues, it’s fine to think about and have Zoom meetings about how to communicate more responsibly than before. “But we also need to have some framework of how to handle it when it’s done wrong,” she says. “Because it will be.”

What is causing MLB’s rash of pitching injuries? Analyzing the data on all the biggest questions

There might not be a solution to the pitching injury problem in baseball. If you sort the research and data on the subject to answer the questions most asked about the subject, you don’t end up in a place where there’s an easy way forward.

But that exercise, of answering what we think we can answer the best way possible, seems like a worthy enterprise either way.

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What is the main source of pitcher injury ?

Velocity. Throwing hard is a direct stressor on the elbow , and throwing hard has been shown to lead to injury by multiple studies over the years . One study found that fastball velocity was the most predictive factor of needing elbow surgery in pro pitchers. Every additional tick is more stress on the elbow ligament.

Are pitchers getting hurt because they are throwers, not pitchers now?

Although this has an element of shaking a fist at clouds, there’s also an element of truth in this. Glenn Fleisig, Jonathan Slowik, et al. found that the closer a pitcher pitches to their maximum velocity, the more stress they put on their elbow, and other studies have found similar answers . The bad news is that baseball, as a sport, is throwing closer to its maximum with every year. And yes, this is keeping the method of measuring that velocity constant, it has nothing to do with radar technology changes.

The hardest tracked throw remains Aroldis Chapman ’s 105.7 mph in 2016, and the league’s maximum has settled in around 105 mph most seasons. But the average fastball just keeps climbing, meaning as a league, pitchers are throwing closer to their maximum. And this is true on the individual level — according to STATS Perform, the difference between the average starting pitcher’s sitting and max fastball velocity (minimum 500 thrown in a season) was down to a pitch-tracking low of 3.2 mph in 2023, and that’s more than a full tick less than where it started when pitch tracking began. Today’s baseball is a max-effort game. Unfortunately, while varying the velocity on the fastball may help a pitcher “save bullets” and reduce stress, it does not help them perform better .

Are analytics to blame?

There’s an obvious bias here between the author and the answer, but this line of questioning does not reflect well on professional pitchers’ ability to understand the risks and rewards in their own sport. In other words, yes, there have been all sorts of studies that link fastball velocity to better outcomes, starting with Atlanta Braves executive Mike Fast’s seminal piece on the subject and culminating most recently in things like Stuff+ , but does a pitcher really need an analyst to tell them they need to get strikeouts, and that throwing hard will get them there? Listen to Justin Verlander on the subject. He’s smart, but he also captures the feeling of most pitchers when faced with the reality of getting outs in today’s game — especially given the rules changes that have favored offense.

Justin Verlander on the rash of pithcer injuries: "…I think the game has changed a lot, it would be easiest to blame the pitch clock, in reality everything has a little bit of influence, the biggest thing is the style of pitching has changed so much, everyone is throwing as… pic.twitter.com/rzmvwhB27R — Ari Alexander (@AriA1exander) April 7, 2024

Pitchers organize their talents to get outs, and pitchers know velocity is good for that. The role of the analyst, as it always has been, is to support the players in getting as many wins as possible. Don’t hate the players (or the analysts), they’re just trying to win games.

Are the injuries because of all the breaking balls?

One of the studies that looked at direct stress on the elbow for different pitch types did find that, although overall velocity was the biggest source of stress, once you adjusted for velocity, breaking balls provided more stress at any given mph . Eighty-five mph is a bit of a magic spot for breaking balls — they get better above that velocity, at least. In 2023, pitchers threw nearly 44,000 more breaking balls over 85 mph than they did when we started tracking pitches in 2008. But this is a distinction without a difference, probably: Whether it’s fastball velocity or breaking ball velocity, it’s still velocity.

What role does the pitch clock have in injury?

Theoretically, if you ask an athlete to do the same amount of work in less time, you’re increasing their fatigue. That’s something so basic it shouldn’t require supporting research, but before Dr. Mike Sonne went to work for the Cubs , he wrote for The Athletic about how that works. And how that fatigue should lead to more injuries .

The weird thing is… it hasn’t. Yet. Not at the major-league level.

Despite an early surge in injuries in the first year of the clock , once the year finished, there was no real discernible difference in injury rates . And because March and April are the biggest months for injury list placements, it wouldn’t make much sense to report this year’s injury rates as a big predictor, not until we sum it all up in the end again. The increase in injuries on the major-league level has been a slow burn, not a big spike. The one caveat is that minor-league UCL injuries have exploded since the pitch clock was first introduced in 2018, going from 152 in 2017 to consistently over 200 in each of the past three seasons. (Then again, there were only 86 UCL injuries in the minors in 2011, so there’s more happening here.)

It’s probably most accurate to say that the clock has some role, but that role is undefined as of now, and there’s a longer trend at play, so it can’t all be the clock.

Does sticky stuff (and its ban) have any role in the injury increase?

Tyler Glasnow famously felt that banning sticky stuff led to him having to grip the ball harder, which led to his injury.

Tyler Glasnow made it very clear why pitchers were getting injured 2 years ago. It’s not the pitch clock Nor even joking, this is all Trevor Bauer’s fault pic.twitter.com/Tr0XN8y4En — Nate (@notNate99) April 7, 2024

Research on grip strength isn’t conclusive. Grip strength probably doesn’t lead to more spin , but could lead to better health outcomes , and doesn’t seem to stress the elbow — but these are all studies about grip strength relative to other players. There may not be a study out there that looks at what happens when a pitcher grips the ball harder than he normally does.

But, again, there’s no real spike in injuries after enforcement. There were 243 pitcher injuries in 2021, 226 in 2022 and 233 last year. Sticky stuff enforcement happened in mid-2021.

Could year-round throwing be the problem?

“You have to build up your fitness,” said Sonne, who is a data scientist for the Cubs now. “If you’re running a marathon, you don’t not run for months so you can run on race day. The April spike in injuries happens because people STOP throwing and then try to build up.”

There might be a difference here between adult professionals and kids, though.

On pitching injuries, I'll say this: start with rigorous adherence to basic protective guidelines before tackling advanced physics/sports medicine challenges. >100 IP/yr is associated with a 350% increased risk of injury in youth arms. If you can count, you can prevent injuries. — Eric Cressey (@EricCressey) April 8, 2024

Throwing 100 innings as your body is still developing looks like it increases injury risk. But that might also be because of the shape of those innings, the effort the young person is putting into those pitches, the amount of rest they get and how their workload was monitored. If those things are inconsistent in MLB , they’re probably near nonexistent on a concise level across youth leagues. Throwing less has been put out there as a solution … except that it doesn’t prepare them all that well for throwing more in the future. Pitchers are throwing less, everywhere, and they’re injured more.

Are there better mechanics out there that could solve the problem?

There have been findings that have come out of the emergent study of biomechanics . Certain relationships between your landing foot, your trunk rotation and your shoulder movement have been deemed better than others. Some think they’ve got the perfect mechanics that will ensure a way out of this problem. But Casey Mulholland, who runs Kinetic Pro, a private player development lab, outlined a problem with blaming it all on mechanics.

“Let’s say you’ve got a pitcher with a three-quarter arm slot — that means more stress, more valgus torque,” Mulholland said. “He comes to Tampa and I magically change his arm action to produce the same velo more over the top, and now he throws with less torque. Well, with the cleaned-up arm action, he can now throw harder. And the one thing we know that increases stress is velo, sooooo.

“Our brain passes messages to our muscles, forearm flexors in this case, via the central nervous system to contract at just the right moment to offload the stress applied to the UCL. When we become fatigued our brain doesn’t pass this message as well, the muscles don’t contract at the ‘optimal time’ or the ‘optimal amount’ and we end up not being able to offload this stress. The UCL then wears more of a direct stress. Over time, under fatigue, the load of throwing eventually overcomes the tissue tolerance and boom, UCL tear.

“This is why workload management is the only logical answer to slow the injury rate,” thinks Mulholland. “Workload management predicts the possible time at which an athlete might experience too much load.”

What rule changes could incentivize different injury outcomes?

Some proposed rule changes are just not going to happen. Every pitch over 94 is a ball? Just can’t see it. Similarly, the idea that we will limit the number of pitchers on the roster might work to prod teams into getting more innings from each pitching slot. But the players’ union would assuredly be against any reduction in major-league jobs.

Jayson Stark’s proposed “Double Hook” — in which the pitching team loses their designated hitter once the starting pitcher leaves the game — would incentivize teams to acquire players who can go deeper into games. But there’s a little bit of a gap there that will be funky for the pitchers themselves. Until the market shows that it will value lesser quality with more quantity (it hasn’t), the pitcher’s incentives may be misaligned with those of the team as a whole. They’d rather be the guy with better stuff and cleaner numbers if they know that’s what free agency has rewarded in the past.

Limiting the number of active pitchers for a single game seems like a bland rule change that might not mean much. But, in light of Mulholland’s and Sonne’s feelings about the importance of workload monitoring, maybe asking teams to fully rule out some number of pitchers every game could nudge teams into the era of more precise workload management.

In the end, there’s no telling pitchers to throw softer if it isn’t going to help their bottom line — and that’s sort of the simplest way to state the problem. The best we can do as a sport is to provide players with the best possible mechanics that analysis can provide, and be as precise as possible with monitoring their fatigue. At the very top of any game, injuries will happen.

From revision surgery to internal brace procedures, understanding Tommy John surgery today

(Photo of Justin Verlander: Carmen Mandato / Getty Images)

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Eno Sarris is a senior writer covering baseball analytics at The Athletic. Eno has written for FanGraphs, ESPN, Fox, MLB.com, SB Nation and others. Submit mailbag questions to [email protected] . Follow Eno on Twitter @ enosarris

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• J Indian Assoc Pediatr Surg
• v.24(1); Jan-Mar 2019

Formulation of Research Question – Stepwise Approach

Simmi k. ratan.

Department of Pediatric Surgery, Maulana Azad Medical College, New Delhi, India

1 Department of Community Medicine, North Delhi Municipal Corporation Medical College, New Delhi, India

2 Department of Pediatric Surgery, Batra Hospital and Research Centre, New Delhi, India

Formulation of research question (RQ) is an essentiality before starting any research. It aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. It is, therefore, pertinent to formulate a good RQ. The present paper aims to discuss the process of formulation of RQ with stepwise approach. The characteristics of good RQ are expressed by acronym “FINERMAPS” expanded as feasible, interesting, novel, ethical, relevant, manageable, appropriate, potential value, publishability, and systematic. A RQ can address different formats depending on the aspect to be evaluated. Based on this, there can be different types of RQ such as based on the existence of the phenomenon, description and classification, composition, relationship, comparative, and causality. To develop a RQ, one needs to begin by identifying the subject of interest and then do preliminary research on that subject. The researcher then defines what still needs to be known in that particular subject and assesses the implied questions. After narrowing the focus and scope of the research subject, researcher frames a RQ and then evaluates it. Thus, conception to formulation of RQ is very systematic process and has to be performed meticulously as research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

I NTRODUCTION

A good research question (RQ) forms backbone of a good research, which in turn is vital in unraveling mysteries of nature and giving insight into a problem.[ 1 , 2 , 3 , 4 ] RQ identifies the problem to be studied and guides to the methodology. It leads to building up of an appropriate hypothesis (Hs). Hence, RQ aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. A good RQ helps support a focused arguable thesis and construction of a logical argument. Hence, formulation of a good RQ is undoubtedly one of the first critical steps in the research process, especially in the field of social and health research, where the systematic generation of knowledge that can be used to promote, restore, maintain, and/or protect health of individuals and populations.[ 1 , 3 , 4 ] Basically, the research can be classified as action, applied, basic, clinical, empirical, administrative, theoretical, or qualitative or quantitative research, depending on its purpose.[ 2 ]

Research plays an important role in developing clinical practices and instituting new health policies. Hence, there is a need for a logical scientific approach as research has an important goal of generating new claims.[ 1 ]

C HARACTERISTICS OF G OOD R ESEARCH Q UESTION

“The most successful research topics are narrowly focused and carefully defined but are important parts of a broad-ranging, complex problem.”

A good RQ is an asset as it:

• Details the problem statement
• Further describes and refines the issue under study
• Adds focus to the problem statement
• Guides data collection and analysis
• Sets context of research.

Hence, while writing RQ, it is important to see if it is relevant to the existing time frame and conditions. For example, the impact of “odd-even” vehicle formula in decreasing the level of air particulate pollution in various districts of Delhi.

A good research is represented by acronym FINERMAPS[ 5 ]

Interesting.

• Appropriate
• Potential value and publishability
• Systematic.

Feasibility means that it is within the ability of the investigator to carry out. It should be backed by an appropriate number of subjects and methodology as well as time and funds to reach the conclusions. One needs to be realistic about the scope and scale of the project. One has to have access to the people, gadgets, documents, statistics, etc. One should be able to relate the concepts of the RQ to the observations, phenomena, indicators, or variables that one can access. One should be clear that the collection of data and the proceedings of project can be completed within the limited time and resources available to the investigator. Sometimes, a RQ appears feasible, but when fieldwork or study gets started, it proves otherwise. In this situation, it is important to write up the problems honestly and to reflect on what has been learned. One should try to discuss with more experienced colleagues or the supervisor so as to develop a contingency plan to anticipate possible problems while working on a RQ and find possible solutions in such situations.

This is essential that one has a real grounded interest in one's RQ and one can explore this and back it up with academic and intellectual debate. This interest will motivate one to keep going with RQ.

The question should not simply copy questions investigated by other workers but should have scope to be investigated. It may aim at confirming or refuting the already established findings, establish new facts, or find new aspects of the established facts. It should show imagination of the researcher. Above all, the question has to be simple and clear. The complexity of a question can frequently hide unclear thoughts and lead to a confused research process. A very elaborate RQ, or a question which is not differentiated into different parts, may hide concepts that are contradictory or not relevant. This needs to be clear and thought-through. Having one key question with several subcomponents will guide your research.

This is the foremost requirement of any RQ and is mandatory to get clearance from appropriate authorities before stating research on the question. Further, the RQ should be such that it minimizes the risk of harm to the participants in the research, protect the privacy and maintain their confidentiality, and provide the participants right to withdraw from research. It should also guide in avoiding deceptive practices in research.

The question should of academic and intellectual interest to people in the field you have chosen to study. The question preferably should arise from issues raised in the current situation, literature, or in practice. It should establish a clear purpose for the research in relation to the chosen field. For example, filling a gap in knowledge, analyzing academic assumptions or professional practice, monitoring a development in practice, comparing different approaches, or testing theories within a specific population are some of the relevant RQs.

Manageable (M): It has the similar essence as of feasibility but mainly means that the following research can be managed by the researcher.

Appropriate (A): RQ should be appropriate logically and scientifically for the community and institution.

Potential value and publishability (P): The study can make significant health impact in clinical and community practices. Therefore, research should aim for significant economic impact to reduce unnecessary or excessive costs. Furthermore, the proposed study should exist within a clinical, consumer, or policy-making context that is amenable to evidence-based change. Above all, a good RQ must address a topic that has clear implications for resolving important dilemmas in health and health-care decisions made by one or more stakeholder groups.

Systematic (S): Research is structured with specified steps to be taken in a specified sequence in accordance with the well-defined set of rules though it does not rule out creative thinking.

Example of RQ: Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? This question fulfills the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant.

Types of research question

A RQ can address different formats depending on the aspect to be evaluated.[ 6 ] For example:

• Existence: This is designed to uphold the existence of a particular phenomenon or to rule out rival explanation, for example, can neonates perceive pain?
• Description and classification: This type of question encompasses statement of uniqueness, for example, what are characteristics and types of neuropathic bladders?
• Composition: It calls for breakdown of whole into components, for example, what are stages of reflux nephropathy?
• Relationship: Evaluate relation between variables, for example, association between tumor rupture and recurrence rates in Wilm's tumor
• Descriptive—comparative: Expected that researcher will ensure that all is same between groups except issue in question, for example, Are germ cell tumors occurring in gonads more aggressive than those occurring in extragonadal sites?
• Causality: Does deletion of p53 leads to worse outcome in patients with neuroblastoma?
• Causality—comparative: Such questions frequently aim to see effect of two rival treatments, for example, does adding surgical resection improves survival rate outcome in children with neuroblastoma than with chemotherapy alone?
• Causality–Comparative interactions: Does immunotherapy leads to better survival outcome in neuroblastoma Stage IV S than with chemotherapy in the setting of adverse genetic profile than without it? (Does X cause more changes in Y than those caused by Z under certain condition and not under other conditions).

How to develop a research question

• Begin by identifying a broader subject of interest that lends itself to investigate, for example, hormone levels among hypospadias
• Do preliminary research on the general topic to find out what research has already been done and what literature already exists.[ 7 ] Therefore, one should begin with “information gaps” (What do you already know about the problem? For example, studies with results on testosterone levels among hypospadias
• What do you still need to know? (e.g., levels of other reproductive hormones among hypospadias)
• What are the implied questions: The need to know about a problem will lead to few implied questions. Each general question should lead to more specific questions (e.g., how hormone levels differ among isolated hypospadias with respect to that in normal population)
• Narrow the scope and focus of research (e.g., assessment of reproductive hormone levels among isolated hypospadias and hypospadias those with associated anomalies)
• Is RQ clear? With so much research available on any given topic, RQs must be as clear as possible in order to be effective in helping the writer direct his or her research
• Is the RQ focused? RQs must be specific enough to be well covered in the space available
• Is the RQ complex? RQs should not be answerable with a simple “yes” or “no” or by easily found facts. They should, instead, require both research and analysis on the part of the writer
• Is the RQ one that is of interest to the researcher and potentially useful to others? Is it a new issue or problem that needs to be solved or is it attempting to shed light on previously researched topic
• Is the RQ researchable? Consider the available time frame and the required resources. Is the methodology to conduct the research feasible?
• Is the RQ measurable and will the process produce data that can be supported or contradicted?
• Is the RQ too broad or too narrow?
• Create Hs: After formulating RQ, think where research is likely to be progressing? What kind of argument is likely to be made/supported? What would it mean if the research disputed the planned argument? At this step, one can well be on the way to have a focus for the research and construction of a thesis. Hs consists of more specific predictions about the nature and direction of the relationship between two variables. It is a predictive statement about the outcome of the research, dictate the method, and design of the research[ 1 ]
• Understand implications of your research: This is important for application: whether one achieves to fill gap in knowledge and how the results of the research have practical implications, for example, to develop health policies or improve educational policies.[ 1 , 8 ]

Brainstorm/Concept map for formulating research question

• First, identify what types of studies have been done in the past?
• Is there a unique area that is yet to be investigated or is there a particular question that may be worth replicating?
• Begin to narrow the topic by asking open-ended “how” and “why” questions
• Evaluate the question
• Develop a Hypothesis (Hs)
• Write down the RQ.

Writing down the research question

• State the question in your own words
• Write down the RQ as completely as possible.

For example, Evaluation of reproductive hormonal profile in children presenting with isolated hypospadias)

• Divide your question into concepts. Narrow to two or three concepts (reproductive hormonal profile, isolated hypospadias, compare with normal/not isolated hypospadias–implied)
• Specify the population to be studied (children with isolated hypospadias)
• Refer to the exposure or intervention to be investigated, if any
• Reflect the outcome of interest (hormonal profile).

Another example of a research question

Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? Apart from fulfilling the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant, it also details about the intervention done (topical skin application of oil), rationale of intervention (as a skin barrier), population to be studied (preterm infants), and outcome (reduces hypothermia).

Other important points to be heeded to while framing research question

• Make reference to a population when a relationship is expected among a certain type of subjects
• RQs and Hs should be made as specific as possible
• Avoid words or terms that do not add to the meaning of RQs and Hs
• Stick to what will be studied, not implications
• Name the variables in the order in which they occur/will be measured
• Avoid the words significant/”prove”
• Avoid using two different terms to refer to the same variable.

Some of the other problems and their possible solutions have been discussed in Table 1 .

Potential problems and solutions while making research question

G OING B EYOND F ORMULATION OF R ESEARCH Q UESTION–THE P ATH A HEAD

Once RQ is formulated, a Hs can be developed. Hs means transformation of a RQ into an operational analog.[ 1 ] It means a statement as to what prediction one makes about the phenomenon to be examined.[ 4 ] More often, for case–control trial, null Hs is generated which is later accepted or refuted.

A strong Hs should have following characteristics:

• Give insight into a RQ
• Are testable and measurable by the proposed experiments
• Have logical basis
• Follows the most likely outcome, not the exceptional outcome.

E XAMPLES OF R ESEARCH Q UESTION AND H YPOTHESIS

Research question-1.

• Does reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients?

Hypothesis-1

• Reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients
• In pediatric patients with esophageal atresia, gap of <2 cm between two segments of the esophagus and proper mobilization of proximal pouch reduces the morbidity and mortality among such patients.

Research question-2

• Does application of mitomycin C improves the outcome in patient of corrosive esophageal strictures?

Hypothesis-2

In patients aged 2–9 years with corrosive esophageal strictures, 34 applications of mitomycin C in dosage of 0.4 mg/ml for 5 min over a period of 6 months improve the outcome in terms of symptomatic and radiological relief. Some other examples of good and bad RQs have been shown in Table 2 .

Examples of few bad (left-hand side column) and few good (right-hand side) research questions

R ESEARCH Q UESTION AND S TUDY D ESIGN

RQ determines study design, for example, the question aimed to find the incidence of a disease in population will lead to conducting a survey; to find risk factors for a disease will need case–control study or a cohort study. RQ may also culminate into clinical trial.[ 9 , 10 ] For example, effect of administration of folic acid tablet in the perinatal period in decreasing incidence of neural tube defect. Accordingly, Hs is framed.

Appropriate statistical calculations are instituted to generate sample size. The subject inclusion, exclusion criteria and time frame of research are carefully defined. The detailed subject information sheet and pro forma are carefully defined. Moreover, research is set off few examples of research methodology guided by RQ:

• Incidence of anorectal malformations among adolescent females (hospital-based survey)
• Risk factors for the development of spontaneous pneumoperitoneum in pediatric patients (case–control design and cohort study)
• Effect of technique of extramucosal ureteric reimplantation without the creation of submucosal tunnel for the preservation of upper tract in bladder exstrophy (clinical trial).

The results of the research are then be available for wider applications for health and social life

C ONCLUSION

A good RQ needs thorough literature search and deep insight into the specific area/problem to be investigated. A RQ has to be focused yet simple. Research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

R EFERENCES

• 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

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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

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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).

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Trisha Greenhalgh, Julie L. Darbyshire & Emma Ladds

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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.

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Correspondence to Trisha Greenhalgh .

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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.

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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.

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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

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Received : 04 December 2023

Accepted : 26 March 2024

Published : 15 April 2024

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

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An artificial womb could build a bridge to health for premature babies

Surgeon Christoph Haller and his research team from Toronto's Hospital for Sick Children are working on technology that could someday result in an artificial womb to help extremely premature babies. Chloe Ellingson for NPR hide caption

Surgeon Christoph Haller and his research team from Toronto's Hospital for Sick Children are working on technology that could someday result in an artificial womb to help extremely premature babies.

TORONTO — A surgical team scurries around a pregnant female pig lying unconscious on an operating table. They're about to take part in an experiment that could help provide a new option to help premature babies survive.

"The ultimate goal of today is to transition a fetus onto that artificial womb," says Dr. Christoph Haller , motioning to a clear rectangular plastic sack with tubes running in and out of it.

"We're transitioning it into an artificial environment that allows the fetus to still maintain its regular physiology," says Haller, a pediatric heart surgeon at The Hospital for Sick Children.

Today, it's a pig fetus that Haller and his colleagues will be using to test their artificial womb. But their hope is that someday, technology like this will help humans survive extremely premature birth and avoid serious complications, such as blindness and permanent damage to lungs and brains.

A nonprofit says preterm births are up in the U.S. — and it's not a partisan issue

"We're basically trying to find a new concept on how to preserve fetuses to allow them to mature more physiologically compared to the regular preterm. That would be the target — to treat extreme premature babies," says Haller, who's also an assistant professor of surgery at the University of Toronto. "This would hopefully be a big deal — a game changer."

NPR was granted exclusive access to watch Haller's team test their artificial womb.

Research like this is generating enormous excitement among doctors who treat babies who are born prematurely , a major cause of infant mortality and disabilities. But the prospect of an artificial womb is prompting a long list of questions.

"I think it's a really promising and fascinating technology," says Dr. Mark Mercurio , a professor of pediatrics who directs the program for biomedical ethics at the Yale School of Medicine. "But certainly it raises ethical concerns and questions that need to be addressed."

The procedure remains highly experimental

A metal tray next to the pig's belly is covered with blue paper. Haller's team just drew a picture of a pig's face on the paper surrounded by the words "Oink. Oink. Oink." and "We ❤ you." Then they laid out the artificial womb on top of it. Some call this kind of contraption a "biobag."

A technician scans the belly of a pregnant pig before an operation to transfer a fetus to an artificial womb. Chloe Ellingson for NPR hide caption

A technician scans the belly of a pregnant pig before an operation to transfer a fetus to an artificial womb.

Next, the surgical team arranges equipment and examines the 10 fetuses in the sow's womb with an ultrasound. Haller uses a clipper to make some last-minute adjustments to tubing he'll stitch into the fetal pig's umbilical cord.

The tubes will supply the fetus's blood with oxygen, remove carbon dioxide from the blood and supply nutrition and medicine.

"I'm MacGyvering stuff here to make things work," he says with a laugh.

Finally, everyone's ready to remove one of the fetuses.

"All right, I think we're going to get started," Haller says, prompting the team to gather tightly around the pig.

Wisps of smoke rise from the pig's belly as Haller makes an incision with an electric scalpel. An assistant suctions the area to keep it dry.

Dr. Christoph Haller performs surgery to remove a fetal pig from the adult pig's womb. Chloe Ellingson for NPR hide caption

Dr. Christoph Haller performs surgery to remove a fetal pig from the adult pig's womb.

"So what you're looking at is basically the uterus. And then in here is the fetus. The head's somewhere here, where I have my hand. The rest of the body is still inside," he says.

After deciding which fetus looks best on the ultrasound, Haller makes another incision in the uterus and pulls out a bright pink fetal piglet. The fetus looks peaceful, like it's sleeping.

Once the fetus is completely out, Haller and his team quickly assess its health and cut the umbilical cord so they can transfer the animal into the artificial womb.

A "biobag" becomes the new womb

After gingerly sliding the fetus into the "biobag," Haller quickly attaches the three umbilical cord tubes. His colleagues fill the bag with a clear, warm liquid meant to mimic amniotic fluid and seal the artificial womb.

"It's going to be a bit of a rocky period now," Haller says.

A fetal pig rests inside an artificial womb. Chloe Ellingson for NPR hide caption

A fetal pig rests inside an artificial womb.

The team carefully monitors the fetus's heart rate, blood pressure and other vital signs. Once it looks stable, the researchers surround the biobag with warmers.

"It's as close to a good transition as you can get I think," Haller says. "I'm excited as if it was a proper human surgery I would say — just because I want to get it right and I want to see the fetus doing well there."

This will go on for hours.

"You may see the fetus starting to have breathing-like movements. But that's what's in line with what's happening in utero too — as if they are training basically a bit. You may see that it kicks its legs," Haller says. "That's what we like to see because it signals a certain level of health."

An artificial womb could be a bridge to better health

If very premature babies can be safely sustained on a device like this for just two or three weeks, it could make all the difference between life and death or a life with severe disabilities and health problems or not, Haller says.

The Toronto group has seen blood clots and heart problems develop. So far, they've only been able to sustain a pig fetus for about a week.

But researchers at Children's Hospital of Philadelphia have safely sustained fetal sheep on a very similar device for four weeks, making the Toronto group and others optimistic the approach will eventually work.

"If this artificial womb technology could sustain a patient even for a period of weeks and get them to a later stage and a bigger size, that could potentially be quite a dramatic change in our field," says Dr. Mike Seed , an associate professor of pediatrics at the University of Toronto who is working with Haller.

Scientific progress prompts ethical concerns

But the possibility of an artificial womb is also raising many questions. When might it be safe to try an artificial womb for a human? Which preterm babies would be the right candidates? What should they be called? Fetuses? Babies?

"It matters in terms of how we assign moral status to individuals," says Mercurio, the Yale bioethicist. "How much their interests — how much their welfare — should count. And what one can and cannot do for them or to them."

But Mercurio is optimistic those issues can be resolved, and the potential promise of the technology clearly warrants pursuing it.

The Food and Drug Administration held a workshop in September 2023 to discuss the latest scientific efforts to create an artificial womb, the ethical issues the technology raises, and what questions would have to be answered before allowing an artificial womb to be tested for humans.

"I am absolutely pro the technology because I think it has great potential to save babies," says Vardit Ravitsky , president and CEO of The Hastings Center, a bioethics think tank.

But there are particular issues raised by the current political and legal environment.

"My concern is that pregnant people will be forced to allow fetuses to be taken out of their bodies and put into an artificial womb rather than being allowed to terminate their pregnancies — basically, a new way of taking away abortion rights," Ravitsky says.

She also wonders: What if it becomes possible to use artificial wombs to gestate fetuses for an entire pregnancy, making natural pregnancy unnecessary?

"Science fiction writers have been playing with this notion for decades. It's not like we never thought about it. It's just different to think about it as a thought experiment and to think about it as something that's potentially around the corner," Ravitsky says. "The scenario of a complete use of artificial wombs could become pretty scary, pretty quickly."

But Haller and his colleagues say the darkest worries are unfounded.

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The u.s. has a high rate of preterm births, and abortion bans could make that worse.

"We've heard people fearing that this translates into women not having to go through a full pregnancy anymore — kind of more like a Matrix -style of dystopian future," Haller says.

"But it would be outrageous to assume that any artificial intervention in any way is better than nature. So if you're not running into problems in your pregnancy, I think there's a lot of evidence that you're better off being born as you should be from what nature intended," he says.

Haller and his colleagues, he says, are just trying to save babies.

"Every tool can be misused," he says. "Like AI — it has its benefits, but if it's not regulated adequately a lot of harm can arise from something like that as well."

Meanwhile, the fetal pig is settling into its new artificial womb.

"I think it looks pretty, pretty comfy and settled," Haller says. "It looks pretty, pretty happy in there. Yeah, it's good."

• premature mortality
• premature birth
• medical technology