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Empirical Research: Quantitative & Qualitative

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Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

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Empirical research  is based on phenomena that can be observed and measured. Empirical research derives knowledge from actual experience rather than from theory or belief. 

Key characteristics of empirical research include:

• Specific research questions to be answered;
• Definitions of the population, behavior, or phenomena being studied;
• Description of the methodology or research design used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys);
• Two basic research processes or methods in empirical research: quantitative methods and qualitative methods (see the rest of the guide for more about these methods).

(based on the original from the Connelly LIbrary of LaSalle University)

Empirical Research: Qualitative vs. Quantitative

Learn about common types of journal articles that use APA Style, including empirical studies; meta-analyses; literature reviews; and replication, theoretical, and methodological articles.

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Quantitative Research

A quantitative research project is characterized by having a population about which the researcher wants to draw conclusions, but it is not possible to collect data on the entire population.

• For an observational study, it is necessary to select a proper, statistical random sample and to use methods of statistical inference to draw conclusions about the population. 
• For an experimental study, it is necessary to have a random assignment of subjects to experimental and control groups in order to use methods of statistical inference.

Statistical methods are used in all three stages of a quantitative research project.

For observational studies, the data are collected using statistical sampling theory. Then, the sample data are analyzed using descriptive statistical analysis. Finally, generalizations are made from the sample data to the entire population using statistical inference.

For experimental studies, the subjects are allocated to experimental and control group using randomizing methods. Then, the experimental data are analyzed using descriptive statistical analysis. Finally, just as for observational data, generalizations are made to a larger population.

Iversen, G. (2004). Quantitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.), Encyclopedia of social science research methods . (pp. 897-898). Thousand Oaks, CA: SAGE Publications, Inc.

Qualitative Research

What makes a work deserving of the label qualitative research is the demonstrable effort to produce richly and relevantly detailed descriptions and particularized interpretations of people and the social, linguistic, material, and other practices and events that shape and are shaped by them.

Qualitative research typically includes, but is not limited to, discerning the perspectives of these people, or what is often referred to as the actor’s point of view. Although both philosophically and methodologically a highly diverse entity, qualitative research is marked by certain defining imperatives that include its case (as opposed to its variable) orientation, sensitivity to cultural and historical context, and reflexivity. 

In its many guises, qualitative research is a form of empirical inquiry that typically entails some form of purposive sampling for information-rich cases; in-depth interviews and open-ended interviews, lengthy participant/field observations, and/or document or artifact study; and techniques for analysis and interpretation of data that move beyond the data generated and their surface appearances. 

Sandelowski, M. (2004).  Qualitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.),  Encyclopedia of social science research methods . (pp. 893-894). Thousand Oaks, CA: SAGE Publications, Inc.

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Qualitative and Quantitative Research

What is "empirical research".

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Empirical research  is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions  to be answered
• Definition of the  population, behavior, or   phenomena  being studied
• Description of the  process  used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology:  sometimes called "research design" --  how to recreate the study -- usually describes the population, research process, and analytical tools
• Results : sometimes called "findings"  --  what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies
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Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

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Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

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You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

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For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

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Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

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Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

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There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Empirical Studies

• An empirical study is research derived from actual observation or experimentation.
• The written articles resulting from empirical studies undergo a rigorous review by experts in the field of study prior to being published in journals.
• After passing this review the articles are published in a scholarly, peer-reviewed, or academic journal.
• Empirical study articles will generally contain the following features: Abstract - This is a summary of the article. Introduction - This is often identified as the hypothesis of the study and describes the researcher's intent.            Method - A description of how the research was conducted. Results - A description of the findings obtained as a result of the research. Most often answers the hypothesis. Conclusion - A description of how/if the findings were successful and the impact made as a result. References - A detailed listing of all resources cited in the article that support the written work.              

Mixed Methods Research

Mixed Methods Research uses strategies from both qualitative and quantitative research processes to provide a greater understanding of the subject matter.

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• Published: 11 April 2024

The role of champions in the implementation of technology in healthcare services: a systematic mixed studies review

• Sissel Pettersen 1 ,
• Hilde Eide 2 &
• Anita Berg 1  

BMC Health Services Research volume  24 , Article number:  456 ( 2024 ) Cite this article

229 Accesses

Metrics details

Champions play a critical role in implementing technology within healthcare services. While prior studies have explored the presence and characteristics of champions, this review delves into the experiences of healthcare personnel holding champion roles, as well as the experiences of healthcare personnel interacting with them. By synthesizing existing knowledge, this review aims to inform decisions regarding the inclusion of champions as a strategy in technology implementation and guide healthcare personnel in these roles.

A systematic mixed studies review, covering qualitative, quantitative, or mixed designs, was conducted from September 2022 to March 2023. The search spanned Medline, Embase, CINAHL, and Scopus, focusing on studies published from 2012 onwards. The review centered on health personnel serving as champions in technology implementation within healthcare services. Quality assessments utilized the Mixed Methods Appraisal Tool (MMAT).

From 1629 screened studies, 23 were included. The champion role was often examined within the broader context of technology implementation. Limited studies explicitly explored experiences related to the champion role from both champions’ and health personnel’s perspectives. Champions emerged as promoters of technology, supporting its adoption. Success factors included anchoring and selection processes, champions’ expertise, and effective role performance.

The specific tasks and responsibilities assigned to champions differed across reviewed studies, highlighting that the role of champion is a broad one, dependent on the technology being implemented and the site implementing it. Findings indicated a correlation between champion experiences and organizational characteristics. The role’s firm anchoring within the organization is crucial. Limited evidence suggests that volunteering, hiring newly graduated health personnel, and having multiple champions can facilitate technology implementation. Existing studies predominantly focused on client health records and hospitals, emphasizing the need for broader research across healthcare services.

Conclusions

With a clear mandate, dedicated time, and proper training, health personnel in champion roles can significantly contribute professional, technological, and personal competencies to facilitate technology adoption within healthcare services. The review finds that the concept of champions is a broad one and finds varied definitions of the champion role concept. This underscores the importance of describing organizational characteristics, and highlights areas for future research to enhance technology implementation strategies in different healthcare settings with support of a champion.

Peer Review reports

Digital health technologies play a transformative role in healthcare service systems [ 1 , 2 ]. The utilization of technology and digitalization is essential for ensuring patient safety, delivering high quality, cost-effective, and sustainable healthcare services [ 3 , 4 ]. The implementation of technology in healthcare services is a complex process that demands systematic changes in roles, workflows, and service provision [ 5 , 6 ].

The successful implementation of new technologies in healthcare services relies on the adaptability of health professionals [ 7 , 8 , 9 ]. Champions have been identified as a key factor in the successful implementation of technology among health personnel [ 10 , 11 , 12 ]. However, they have rarely been studied as an independent strategy; instead, they are often part of a broader array of strategies in implementation studies (e.g., Hudson [ 13 ], Gullslett and Bergmo [ 14 ]). Prior research has frequently focused on determining the presence or absence of champions [ 10 , 12 , 15 ], as well as investigating the characteristics of individuals assuming the champion role (e.g., George et al. [ 16 ], Shea and Belden [ 17 ]).

Recent reviews on champions [ 18 , 19 , 20 ] have studied their effects on adherence to guidelines, implementation of innovations and facilitation of evidence-based practice. While these reviews suggest that having champions yields positive effects, they underscore the importance for studies that offer detailed insights into the champion’s role concerning specific types of interventions.

There is limited understanding of the practical role requirements and the actual experiences of health personnel in performing the champion role in the context of technology implementation within healthcare services. Further, this knowledge is needed to guide future research on the practical, professional, and relational prerequisites for health personnel in this role and for organizations to successfully employ champions as a strategy in technology implementation processes.

This review seeks to synthesize the existing empirical knowledge concerning the experiences of those in the champion role and the perspectives of health personnel involved in technology implementation processes. The aim is to contribute valuable insights that enhance our understanding of practical role requirements, the execution of the champion role, and best practices in this domain.

The term of champions varies [ 10 , 19 ] and there is a lack of explicit conceptualization of the term ‘champion’ in the implementation literature [ 12 , 18 ]. Various terms for individuals with similar roles also exist in the literature, such as implementation leader, opinion leader, facilitator, change agent, superuser and facilitator. For the purpose of this study, we have adopted the terminology utilized in the recent review by Rigby, Redley and Hutchinson [ 21 ] collectively referring to these roles as ‘champions’. This review aims to explore the experiences of health personnel in their role as champions and the experiences of health personnel interacting with them in the implementation of technology in the healthcare services.

Prior review studies on champions in healthcare services have employed various designs [ 10 , 18 , 19 , 20 ]. In this review, we utilized a comprehensive mixed studies search to identify relevant empirical studies [ 22 ]. The search was conducted utilizing the Preferred Reporting Items for Systematic and Meta-Analysis (PRISMA) guidelines, ensuring a transparent and comprehensive overview that can be replicated or updated by others [ 23 ]. The study protocol is registered in PROSPERO (ID CRD42022335750), providing a more comprehensive description of the methods [ 24 ]. A systematic mixed studies review, examining research using diverse study designs, is well-suited for synthesizing existing knowledge and identifying gaps by harnessing the strengths of both qualitative and quantitative methods [ 22 ]. Our search encompassed qualitative, quantitative, and mixed methods design to capture experiences with the role of champions in technology implementation.

Search strategy and study selection

Search strategy.

The first author, in collaboration with a librarian, developed the search strategy based on initial searches to identify appropriate terms and truncations that align with the eligibility criteria. The search was constructed utilizing a combination of MeSH terms and keywords related to technology, implementation, champion, and attitudes/experiences. Conducted in August/September 2022, the search encompassed four databases: Medline, Embase, CINAHL, and Scopus, with an updated search conducted in March 2023. The full search strategy for Medline is provided in Appendix  1 . The searches in Embase, CINAHL and Scopus employed the same strategy, with adopted terms and phrases to meet the requirements of each respective database.

Eligibility criteria

We included all empirical studies employing qualitative, quantitative, and mixed methods designs that detailed the experiences and/or attitudes of health personnel regarding the champions role in the implementation of technology in healthcare services. Articles in the English language published between 2012 and 2023 were considered. The selected studies involved technology implemented or adapted within healthcare services.

Conference abstract and review articles were excluded from consideration. Articles published prior 2012 were excluded as a result of the rapid development of technology, which could impact the experiences reported. Furthermore, articles involving surgical technology and pre-implementation studies were also excluded, as the focus was on capturing experiences and attitudes from the adoption and daily use of technology. The study also excluded articles that involved champions without clinical health care positions.

Study selection

A total of 1629 studies were identified and downloaded from the selected databases, with Covidence [ 25 ] utilized as a software platform for screening. After removing 624 duplicate records, all team members collaborated to calibrate the screening process utilizing the eligibility criteria on the initial 50 studies. Subsequently, the remaining abstracts were independently screened by two researchers, blinded to each other, to ensure adherence to the eligibility criteria. Studies were included if the title and abstract included the term champion or its synonyms, along with technology in healthcare services, implementation, and health personnel’s experiences or attitudes. Any discrepancies were resolved through consensus among all team members. A total of 949 abstracts were excluded for not meeting this inclusion condition. During the initial search, 56 remaining studies underwent full-text screening, resulting in identification of 22 studies qualified for review.

In the updated search covering the period September 2022 to March 2023, 64 new studies were identified. Of these, 18 studies underwent full-text screening, and one study was included in our review. The total number of included studies is 23. The PRISMA flowchart (Fig.  1 ) illustrates the process.

Flow Chart illustrating the study selection and screening process

Data extraction

The research team developed an extraction form for the included studies utilizing an Excel spreadsheet. Following data extraction, the information included the Name of Author(s) Year of publication, Country/countries, Title of the article, Setting, Aim, Design, Participants, and Sample size of the studies, Technology utilized in healthcare services, name/title utilized to describe the Champion Role, how the studies were analyzed and details of Attitude/Experience with the role of champion. Data extraction was conducted by SP, and the results were deliberated in a workshop with the other researchers AB, and HE until a consensus was reached. Any discrepancies were resolved through discussions. The data extraction was categorized into three categories: qualitative, quantitative, and mixed methods, in preparation for quality appraisal.

Quality appraisal

The MMAT [ 26 ] was employed to assess the quality of the 23 included studies. Specifically designed for mixed studies reviews, the MMAT allows for the appraisal of the methodological quality of studies falling into five categories. The studies in our review encompassed qualitative, quantitative descriptive, and mixed methods studies. The MMAT begins with two screening questions to confirm the empirical nature of this study. Subsequently, all studies were categorized by type and evaluated utilizing specific criteria based on their research methods, with ratings of ‘Yes,’ ‘No’ or ‘Can’t tell.’ The MMAT discourages overall scores in favor of providing a detailed explanation for each criterion. Consequently, we did not rely on the MMAT’s overall methodical quality scores and continued to include all 23 studies for our review. Two researchers independently scored the studies, and any discrepancies were discussed among all team members until a consensus was reached. The results of the MMAT assessments are provided in Appendix  2 .

Data synthesis

Based on discussions of this material, additional tables were formulated to present a comprehensive overview of the study characteristics categorized by study design, study settings, technology included, and descriptions/characteristics of the champion role. To capture attitudes and experiences associated with the champion role, the findings from the included studies were translated into narrative texts [ 22 ]. Subsequently, the reviewers worked collaboratively to conduct a thematic analysis, drawing inspiration from Braun and Clarke [ 27 ]. Throughout the synthesis process, multiple meetings were conducted to discern and define the emerging themes and subthemes.

The adopting of new technology in healthcare services can be perceived as both an event and a process. According to Iqbal [ 28 ], experience is defined as the knowledge and understanding gained after an event or the process of living through or undergoing an event. This review synthesizes existing empirical knowledge regarding the experiences of occupying the champion role, and the perspectives of health personnel interacting with champions in technology implementation processes.

Study characteristics

The review encompassed a total of 23 studies, and an overview of these studies is presented in Table  1 . Of these, fourteen studies employed a qualitative design, four had quantitative design, and five utilized a mixed method design. The geographical distribution revealed that the majority of studies were conducted in the USA (8), followed by Australia (5), England (4), Canada (2), Norway (2), Ireland (1), and Malaysia (1). In terms of settings, 11 studies were conducted in hospitals, five in primary health care, three in home-based care settings, and four in a mixed settings where two or more settings collaborated. Various technologies were employed across these studies, with client health records (7) and telemedicine (5) being the most frequently utilized. All studies included experiences from champions or health personnel collaborating with champions in their respective healthcare services. Only three studies had the champion role as a main objective [ 29 , 30 , 31 ]. The remaining studies described champions as one of the strategies in technology implementation processes, including 10 evaluation studies (including feasibility studies [ 32 , 33 , 34 ] and one cost-benefit study [ 30 ]).

Several studies underscored the importance of champions for successful implementation [ 29 , 30 , 31 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 43 , 49 ]. Four studies specifically highlighted champions as a key factor for success [ 34 , 36 , 37 , 43 ], and one study went further to describe champions as the most important factor for successful implementation [ 39 ]. Additionally, one study associated champions with reduced labor cost [ 30 ].

Thin descriptions, yet clear expectations for technology champions’ role and -attributes

The analyses revealed that the concept of champions in studies pertaining to technology implementation in healthcare services varies, primarily as a result of the diversity of terms utilized to describe the role combined with short role descriptions. Nevertheless, the studies indicated clear expectations for the champion’s role and associated attributes.

The term champion

The term champion was expressed in 20 different forms across the 23 studies included in our review. Three studies utilized multiple terms within the same study [ 32 , 47 , 48 ] and 15 different authors [ 29 , 32 , 33 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 50 ] employed the term with different compositions (Table  1 ). Furthermore, four authors utilized the term Super user [ 30 , 31 , 49 , 51 ], while four authors employed the terms Facilitator [ 38 ], IT clinician [ 48 ], Leader [ 45 ], and Manager [ 34 ], each in combination with more specific terms (such as local opinion leaders, IT nurse, or practice manager).

Most studies associated champion roles with specific professions. In seven studies, the professional title was explicitly linked to the concept of champions, such as physician champions or clinical nurse champions, or through the strategic selection of specific professions [ 29 , 33 , 36 , 40 , 43 , 47 , 50 ]. Additionally, some studies did not specify professions, but utilized terms like clinicians [ 45 ] or health professionals [ 41 ].

All included articles portray the champion’s role as facilitating implementation and daily use of technology among staff. In four studies, the champion’s role was not elaborated beyond indicating that the individual holding the role is confident with an interest in technology [ 35 , 41 , 42 , 44 ]. The champion’s role was explicitly examined in six studies [ 29 , 30 , 31 , 33 , 46 , 50 ]. Furthermore, seven studies described the champion in both the methods and results [ 32 , 36 , 38 , 47 , 48 , 49 , 51 ]. In ten of the studies, champions were solely mentioned in the results [ 34 , 35 , 37 , 39 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ].

Eight studies provided a specific description or definition of the champion [ 29 , 30 , 31 , 32 , 38 , 48 , 49 , 50 ]. The champion’s role was described as involving training in the specific technology, being an expert on the technology, providing support and assisting peers when needed. In some instance, the champion had a role in leading the implementation [ 50 ], while in other situations, the champion operated as a mediator [ 48 ].

The champions tasks

In the included studies, the champion role encompassed two interrelated facilitators tasks: promoting the technology and supporting others in adopting the technology in their daily practice. Promoting the technology involved encouraging staff adaptation [ 32 , 34 , 35 , 37 , 40 , 41 , 49 ], generally described as being enthusiastic about the technology [ 32 , 35 , 37 , 41 , 48 ], influencing the attitudes and beliefs of colleagues [ 42 , 45 ] and legitimizing the introduction of the technology [ 42 , 46 , 48 ]. Supporting others in technology adaption involved training and teaching [ 31 , 35 , 38 , 40 , 51 ], as well as providing technical support [ 30 , 31 , 39 , 43 , 49 ] and social support [ 49 ]. Only four studies reported that the champions received their own training to enable them able to support their colleagues [ 30 , 31 , 39 , 48 ]. Furthermore, eight studies [ 32 , 34 , 38 , 40 , 48 , 49 , 50 , 51 ], specified that the champion role included leadership and management responsibilities, mentioning tasks such as planning, organizing, coordinating, and mediating technology adaption without providing further details.

Desirable champion attributes

To effectively fulfill their role, champions should ideally possess clinical expertise and experience [ 29 , 35 , 38 , 40 , 48 ], stay professionally updated [ 37 , 48 ], and possess knowledge of the organization and workflows [ 29 , 34 , 46 ]. They should have the ability to understand and communicate effectively with healthcare personnel [ 31 , 32 , 46 , 49 ] and be proficient in IT language [ 51 ]. Moreover, champions should demonstrate a general technological interest and competence, and competence, along with specific knowledge of the technology to be implemented [ 32 , 37 , 49 ]. It is also emphasized that they should command formal and/or informal respect and authority in the organization [ 36 , 45 ], be accessible to others [ 39 , 43 ], possess leadership qualities [ 34 , 37 , 38 , 46 ], and understand and balance the needs of stakeholders [ 43 ]. Lastly, the champions should be enthusiastic promoters of the technology, engaging and supporting others [ 31 , 32 , 33 , 34 , 37 , 39 , 40 , 41 , 43 , 49 ], while also effectively coping with cultural resistance to change [ 31 , 46 ].

Anchoring and recruiting for the champion role

The champions were organized differently within services, holding various positions in the organizations, and being recruited for the role in different ways.

Anchoring the champion role

The champion’s role is primarily anchored at two levels: the management level and/or the clinical level, with two studies having champions at both levels [ 34 , 49 ]. Those working with the management actively participated in the planning of the technology implementation [ 29 , 36 , 40 , 41 , 45 ]. Serving as advisors to management, they leveraged their clinical knowledge to guide the implementation in alignment with the necessities and possibilities of daily work routines in the clinics. Champions in this capacity experienced having a clear formal position that enabled them to fulfil their role effectively [ 29 , 40 ]. Moreover, these champions served as bridge builders between the management and department levels [ 36 , 45 ], ensuring the necessary flow of information in both directions.

Champions anchored at the clinic level played a pivotal role in the practical implementation and facilitation of the daily use of technology [ 31 , 33 , 35 , 37 , 38 , 43 , 48 , 51 ]. Additionally, these champions actively participated in meetings with senior management to discuss the technology and its implementation in the clinic. This position conferred potential influence over health personnel [ 33 , 35 ]. Champions at the clinic level facilitated collaboration between employees, management, and suppliers [ 48 ]. Fontaine et al. [ 36 ] identified respected champions at the clinical level, possessing authority and formal support from all leadership levels, as the most important factor for success.

Only one study reported that the champions received additional compensation for their role [ 36 ], while another study mentioned champions having dedicated time to fulfil their role [ 46 ]. The remaining studies did not provide this information.

Recruiting for the role as champion

Several studies have reported different experiences regarding the management’s selection of champions. A study highlighted the distinctions between a volunteered role and an appointed champion’s role [ 31 ]. Some studies underscored that appointed champions were chosen based on technological expertise and skills [ 41 , 48 , 51 ]. Moreover, the selection criteria included champions’ interest in the specific technology [ 42 ] or experiential skills [ 40 ]. The remaining studies did not provide this information.

While the champion role was most frequently held by health personnel with clinical experience, one study deviated by hiring 150 newly qualified nurses as champions [ 30 ] for a large-scale implementation of an Electronic Health Record (EHR). Opting for clinical novices assisted in reducing implementation costs, as it avoided disrupting daily tasks and interfering with daily operations. According to Bullard [ 30 ], these super-user nurses became highly sought after post-implementation as a result of their technological confidence and competence.

Reported experiences of champions and health personnel

Drawing from the experiences of both champions and health personnel, it is essential for a champion to possess a combination of general knowledge and specific champion characteristics. Furthermore, champions are required to collaborate with individuals both within and outside the organization. The subsequent paragraphs delineate these experiences, categorizing them into four subsets: champions’ contextual knowledge and expertise, preferred performance of the champion role, recognizing that a champion alone is insufficient, and distinguishing between reactive and proactive champions.

Champions’ contextual knowledge and know-how

Health personnel with experience interacting with champions emphasized that a champion must be familiar with the department and its daily work routines [ 35 , 40 ]. Knowledge of the department’s daily routines made it easier for champions to facilitate the adaptation of technology. However, there was a divergence of opinions on whether champions were required to possess extensive clinical experience to fulfil their role. In most studies, having an experienced and competent clinician as a champion instilled a sense of confidence among health personnel. Conversely, Bullard’s study [ 30 ] exhibited that health personnel were satisfied with newly qualified nurses in the role of champion, despite their initial skepticism.

It is a generally expected that champions should possess technological knowledge beyond that of other health professionals [ 37 , 41 ]. Some health personnel perceived the champions as uncritical promoters of technology, with the impression that health personnel were being compelled to utilize technology [ 46 ]. Champions could also overestimate the readiness of health personnel to implement a technology, especially during the early phases of the implementation process [ 32 ]. Regardless of whether the champion is at the management level or the clinic level, champions themselves have acknowledged the importance of providing time and space for innovation. Moreover, the recruitment of champions should span all levels of the organization [ 34 , 46 ]. Furthermore, champions must be familiar with daily work routines, work tools, and work surfaces [ 38 , 40 , 43 ].

Preferable performance of the champion role

The studies identified several preferable characteristics of successful champions. Health personnel favored champions utilizing positive words when discussing technology and exhibiting positive attitudes while facilitating and adapting it [ 33 , 34 , 37 , 38 , 41 , 46 ]. Additionally, champions who were enthusiastic and engaging were considered good role models for the adoption of technology. Successful champions were perceived as knowledgeable and adept problem solvers who motivated and supported health personnel [ 41 , 43 , 44 , 48 ]. They were also valued for being available and responding promptly when contacted [ 42 ]. Health professionals noted that champions perceived as competent garnered respect in the organization [ 40 ]. Moreover, some health personnel felt that some certain champions wielded a greater influence based on how they encouraged the use of the system [ 48 ]. It was also emphasized that health personnel needed to feel it was safe to provide feedback to champions, especially when encountering difficulties or uncertainties [ 49 ].

A champion is not enough

The role of champions proved to be more demanding than expected [ 29 , 31 , 38 ], involving tasks such as handling an overwhelming number of questions or actively participating in the installation process to ensure the technology functions effectively in the department [ 29 ]. Regardless of the organizational characteristics or the champion’s profile, appointing the champion as a “solo implementation agent” is deemed unsuitable. If the organization begins with one champion, it is recommended that this individual promptly recruits others into the role [ 42 ].

Health personnel, reliant on champions’ expertise, found it beneficial to have champions in all departments, and these champions had to be actively engaged in day-to-day operations [ 31 , 33 , 34 , 37 ]. Champions themselves also noted that health personnel increased their technological expertise through their role as champions in the department [ 39 ].

Furthermore, the successful implementation of technology requires the collaboration of various professions and support functions, a task that cannot be solely addressed by a champion [ 29 , 43 , 48 ]. In Orchard et. al.‘s study [ 34 ], champions explicitly emphasized the necessity of support from other personnel in the organization, such as those responsible for the technical aspects and archiving routines, to provide essential assistance.

According to health personnel, the role of champions is vulnerable in case they become sick or leave their position [ 42 , 51 ]. In some of the included studies, only one or a few hold the position of champion [ 37 , 38 , 42 , 48 ]. Two studies observed that their implementations were not completed because champions left or reassigned for various reasons [ 32 , 51 ]. The health professionals in the study by Owens and Charles [ 32 ] expressed that champions must be replaced in such cases. Further, the study of Olsen et al., 2021 [ 42 ] highlights the need for quicky building a champion network within the organization.

Reactive and proactive champions

Health personnel and champions alike noted that champions played both a reactive and proactive role. The proactive role entailed facilitating measures such as training and coordination [ 31 , 32 , 33 , 34 , 37 , 39 , 40 , 41 , 43 , 48 , 49 ] as initiatives to generate enthusiasm for the technology [ 31 , 32 , 33 , 34 , 35 , 37 , 39 , 40 , 41 , 43 , 49 ]. On the other hand, the reactive role entailed hands-on support and troubleshooting [ 30 , 31 , 39 , 43 , 49 ].

In a study presenting experiences from both health personnel and champions, Yuan et al. [ 31 ] found that personnel observed differences in the assistance provided by appointed and self-chosen champions. Appointed champions demonstrated the technology, answered questions from health personnel, but quickly lost patience and track of employees who had received training [ 31 ]. Health personnel perceived that self-chosen champions were proactive and well-prepared to facilitate the utilization of technology, communicating with the staff as a group and being more competent in utilizing the technology in daily practice [ 31 ]. Health personnel also noted that volunteer champions were supportive, positive, and proactive in promoting the technology, whereas appointed champions acted on request and had a more reactive approach [ 31 ].

This review underscores the breadth of the concept of champion and the significant variation in the champion’s role in implementation of technology in healthcare services. This finding supports the results from previous reviews [ 10 , 18 , 19 , 20 ]. The majority of studies meeting our inclusion criteria did not specifically focus on the experiences of champions and health personnel regarding the champion role, with the exception of studies by Bullard [ 30 ], Gui et al. [ 29 ], Helmer-Smith et al. [ 33 ], Hogan-Murphy et al. [ 46 ], Rea et al. [ 50 ], and Yuan et al. [ 31 ].

The 23 studies encompassed in this review utilized 20 different terms for the champion role. In most studies, the champion’s role was briefly described in terms of the duties it entailed or should entail. This may be linked to the fact that the role of champions was not the primary focus of the study, but rather one of the strategies in the implementation process being investigated. This result reinforces the conclusions drawn by Miech et al. [ 10 ] and Shea et al. [ 12 ] regarding the lack of united understandings of the concept. Furthermore, in Santos et al.‘s [ 19 ] review, champions were only operationalized through presence or absence in 71.4% of the included studies. However, our review finds that there is a consistent and shared understanding that champions should promote and support technology implementation.

Several studies advocate for champions as an effective and recommended strategy for implementing technology [ 30 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 45 , 46 ]. However, we identified that few studies exclusively explore health personnel`s experiences within the champion role when implementing technology in healthcare services.

This suggests a general lack of information essential for understanding the pros, cons, and prerequisites for champions as a strategy within this field of knowledge. However, this review identifies, on a general basis, the types of support and structures required for champions to perform their role successfully from the perspectives of health personnel, contributing to Shea’s conceptual model [ 12 ].

Regarding the organization of the role, this review identified champions holding both formal appointed and informal roles, working in management or clinical settings, being recruited for their clinical and/or technological expertise, and either volunteering or being hired with specific benefits for the role. Regardless of these variations, anchoring the role is crucial for both the individuals holding the champion role and the health personnel interacting with them. Anchoring, in this context, is associated with the clarity of the role’s content and a match between role expectations and opportunities for fulfilment. Furthermore, the role should be valued by the management, preferably through dedicated time and/or salary support [ 34 , 36 , 46 ]. Additionally, our findings indicate that relying on a “solo champion” is vulnerable to issues such as illness, turnover, excessive workload, and individual champion performance [ 32 , 37 ]. Based on these insights, it appears preferable to appoint multiple champions, with roles at both management and clinical levels [ 33 ].

Some studies have explored the selection of champions and its impact on role performance, revealing diverse experiences [ 30 , 31 ]. Notably, Bullard [ 30 ], stands out for emphasizing long clinical experience, and hiring newly trained nurses as superusers to facilitate the use of electronic health records. Despite facing initial reluctance, these newly trained nurses gradually succeeded in their roles. This underscores the importance of considering contextual factors in the champion selection [ 30 , 52 ]. In Bullard’s study [ 30 ], the collaboration between newly trained nurses as digital natives and clinical experienced health personnel proved beneficial, highlighting the need to align champion selection with the organization’s needs based on personal characteristics. This finding aligns with Melkas et al.‘s [ 9 ] argument that implementing technology requires a deeper understanding of users, access to contextual know-how, and health personnel’s tacit knowledge.

To meet role expectations and effectively leverage their professional and technological expertise, champions should embody personal qualities such as the ability to engage others, take a leadership role, be accessible, supportive, and communicate clearly. These qualities align with the key attributes for change in healthcare champions described by Bonawitz et al. [ 15 ]. These attributes include influence, ownership, physical presence, persuasiveness, grit, and a participative leadership style (p.5). These findings suggest that the active performance of the role, beyond mere presence, is crucial for champions to be a successful strategy in technology implementation. Moreover, the recruitment process is not inconsequential. Identifying the right person for the role and providing them with adequate training, organizational support, and dedicated time to fulfill their responsibilities emerge as an important factor based on the insights from champions and health personnel.

Strengths and limitations

While this study benefits from identifying various terms associated with the role of champions, it acknowledges the possibility of missing some studies as a result of diverse descriptions of the role. Nonetheless, a notable strength of the study lies in its specific focus on the health personnel’s experiences in holding the champion role and the broader experiences of health personnel concerning champions in technology implementation within healthcare services. This approach contributes valuable insights into the characteristics of experiences and attitudes toward the role of champions in implementing technology. Lastly, the study emphasizes the relationship between the experiences with the champion role and the organizational setting’s characteristics.

The champion role was frequently inadequately defined [ 30 , 33 , 34 , 35 , 36 , 37 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 51 ], aligning with previous reviews [ 17 , 19 , 21 ]. As indicated by van Laere and Aggestam [ 52 ], this lack of clarity complicates the identification and comparison of champions across studies. Studies that lacking a distinct definition of the champion’s role were consequently excluded. Only studies written in English were included, introducing the possibility of overlooking relevant studies based on our chosen terms for identifying the champion’s role. Most of the included studies focused on technology implementation in a general context, with champions being just one of several measures. This approach resulted in scant descriptions, as champions were often discussed in the results, discussion, or implications sections rather than being the central focus of the research.

As highlighted by Hall et al. [ 18 ]., methodological issues and inadequate reporting in studies of the champion role create challenges for conducting high-quality reviews, introducing uncertainty around the findings. We have adopted a similar approach to Santos et al. [ 19 ], including all studies even when some issues were identified during the quality assessment. Our review shares the same limitations as previous review by Santos et al. [ 19 ] on the champion role.

Practical implications, policy, and future research

The findings emphasize the significance of the relationship between experiences with the champion role and characteristics of organizational settings as crucial factors for success in the champion role. Clear anchoring of the role within the organization is vital and may impact routines, workflows, staffing, and budgets. Despite limited evidence on the experience of the champion’s role, volunteering, hiring newly graduated health personnel, and appointing more than one champion are identified as facilitators of technology implementation. This study underscores the need for future empirical research including clear descriptions of the champion roles, details on study settings and the technologies to be adopted. This will enable the determination of outcomes and success factors in holding champions in technology implementation processes, transferability of knowledge between contexts and technologies as well as enhance the comparability of studies. Furthermore, there is a need for studies to explore experiences with the champion role, preferably from the perspective of multiple stakeholders, as well as focus on the champion role within various healthcare settings.

This study emphasizes that champions can hold significant positions when provided with a clear mandate, dedicated time, and training, contributing their professional, technological, and personal competencies to expedite technology adoption within services. It appears to be an advantage if the health personnel volunteer or apply for the role to facilitate engaged and proactive champions. The implementation of technology in healthcare services demands efforts from the entire service, and the experiences highlighted in this review exhibits that champions can play an important role. Consequently, empirical studies dedicated to the champion role, employing robust designs based current knowledge, are still needed to provide solid understanding of how champions can be a successful initiative when implementing technology in healthcare services.

Data availability

This review relies exclusively on previously published studies. The datasets supporting the conclusions of this article are included within the article and its supplementary files: Description and characteristics of included studies in Table  1 , Study characteristics. The search strategy is provided in Appendix  1 , and the Critical Appraisal Summary of included studies utilizing MMAT is presented in Appendix  2 .

Abbreviations

Electronic Health Record

Implementation Outcomes Framework

Preferred Reporting Items for Systematics and Meta-Analysis

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Acknowledgements

We would like to thank the librarian Malin E. Norman, at Nord university, for her assistance in the development of the search, as well as guidance regarding the scientific databases.

This study is a part of a PhD project undertaken by the first author, SP, and funded by Nord University, Norway. This research did not receive any specific grant from funding agencies in the public, commercial, as well as not-for-profit sectors.

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The first author/SP has been the project manager and was mainly responsible for all phases of the study. The second and third authors HE and AB have contributed to screening, quality assessment, analysis and discussion of findings. Drafting of the final manuscript has been a collaboration between the first/SP and third athor/AB. The final manuscript has been approved by all authors.

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Pettersen, S., Eide, H. & Berg, A. The role of champions in the implementation of technology in healthcare services: a systematic mixed studies review. BMC Health Serv Res 24 , 456 (2024). https://doi.org/10.1186/s12913-024-10867-7

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• http://orcid.org/0000-0002-8129-8376 Jane Ferguson 1 ,
• http://orcid.org/0000-0001-9325-3362 Gemma Stringer 2 ,
• http://orcid.org/0000-0002-0696-480X Kieran Walshe 2 ,
• http://orcid.org/0000-0002-2972-7911 Thomas Allen 3 , 4 ,
• http://orcid.org/0000-0003-1621-8648 Christos Grigoroglou 3 ,
• http://orcid.org/0000-0002-2958-915X Darren M Ashcroft 5 ,
• http://orcid.org/0000-0001-6450-5815 Evangelos Kontopantelis 6 , 7
• 1 Health Services Management Centre, School of Social Policy , University of Birmingham , Birmingham , UK
• 2 Alliance Manchester Business School , University of Manchester , Manchester , UK
• 3 Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care , University of Manchester , Manchester , UK
• 4 Danish Centre for Health Economics , University of Southern Denmark , Odense , Denmark
• 5 NIHR Greater Manchester Patient Safety Research Collaboration (PSRC), Division of Pharmacy and Optometry, Faculty of Biology Medicine and Health , University of Manchester , Manchester , UK
• 6 Division of Informatics, Imaging and Data Sciences , University of Manchester , Manchester , UK
• 7 NIHR School for Primary Care Research, Centre for Primary Care, Division of Population Health, Health Services Research and Primary Care , University of Manchester , Manchester , UK
• Correspondence to Dr Jane Ferguson, Health Services Management Centre, University of Birmingham, Birmingham, UK; j.ferguson.1{at}bham.ac.uk

Background The use of temporary doctors, known as locums, has been common practice for managing staffing shortages and maintaining service delivery internationally. However, there has been little empirical research on the implications of locum working for quality and safety. This study aimed to investigate the implications of locum working for quality and safety.

Methods Qualitative semi-structured interviews and focus groups were conducted with 130 participants, including locums, patients, permanently employed doctors, nurses and other healthcare professionals with governance and recruitment responsibilities for locums across primary and secondary healthcare organisations in the English NHS. Data were collected between March 2021 and April 2022. Data were analysed using reflexive thematic analysis and abductive analysis.

Results Participants described the implications of locum working for quality and safety across five themes: (1) ‘familiarity’ with an organisation and its patients and staff was essential to delivering safe care; (2) ‘balance and stability’ of services reliant on locums were seen as at risk of destabilisation and lacking leadership for quality improvement; (3) ‘discrimination and exclusion’ experienced by locums had negative implications for morale, retention and patient outcomes; (4) ‘defensive practice’ by locums as a result of perceptions of increased vulnerability and decreased support; (5) clinical governance arrangements, which often did not adequately cover locum doctors.

Conclusion Locum working and how locums were integrated into organisations posed some significant challenges and opportunities for patient safety and quality of care. Organisations should take stock of how they work with the locum workforce to improve not only quality and safety but also locum experience and retention.

• Health services research
• Patient safety
• Qualitative research
• Quality improvement

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No data are available.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjqs-2023-016699

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Despite longstanding policy concerns about the implications of locum working for quality and safety, there has been little empirical research. Understanding how organisations engage, support and work with locums and how locum doctors integrate and interact with the complex and changing systems in which they work is essential if quality and safety are to be improved.

WHAT THIS STUDY ADDS

This qualitative study examines the perspectives of locums, patients and people who work with locums to identify the implications of temporary medical working for quality and safety.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Organisations should examine how they engage, support and work with locums. Organisations and locums need to reflect on whether their practices support a collective approach to patient safety and quality of care.

Introduction

Temporary doctors, often known as locums, are a vital resource that enable healthcare organisations to deliver care by flexing capacity and covering staffing gaps. In the United Kingdom, all doctors, other than those in their first year of training after qualifying, can work as a locum. Locum work can vary from very short-term (a single shift) to longer-term assignments (weeks, months or even sometimes years). Locums find work through various platforms, including locum agencies, online job platforms, professional networks or word of mouth. Locum agencies typically have some governance responsibilities (such as compliance with regulations and licensing requirements), but the extent of these responsibilities varies and the NHS in England has no oversight over how recruitment agencies operate. Despite concerns among policymakers, healthcare providers, professional associations and professional regulators about the implications of locum working for quality and safety and cost, 1–3 there is limited robust empirical research to evidence or support those concerns.

The workforce retention crisis is a significant challenge in healthcare internationally 4–6 and persistent understaffing poses a serious risk to patient safety. 7 8 In the UK, high doctor turnover has been linked to poorer service and health outcomes 9 and has led NHS trusts and general practices (GPs) to be ‘overly reliant’ 3 on temporary staff to fill rota gaps. 10 11 Expenditure on temporary staff in the NHS in England increased from £3.45 billion to £5.2 billion between 2021 and 2022. 3 12 The NHS Long Term Workforce Plan aims to reduce reliance on temporary staff and make substantive employment the most cost-effective and attractive option. 3 However, with the vacancy rate in the NHS projected to increase, 13 locums are likely to continue to be essential to maintaining service provision, especially in shortage specialities such as psychiatry. 14

An obvious implication of locum working is a reduced likelihood of organisational and team integration, 15 familiarity and a shared understanding of ‘the way things are done around here’. 16 Locums are likely to be less familiar with teams and other contextual factors relevant to providing safe and effective care 17 and more likely to be situated on the periphery of organisational structures, teams and governance systems 1 18 Teamwork represents a powerful process to improve patient care, 19 20 and trust, shared understanding, communication and collaboration have been associated with better patient outcomes. 21 22 The ability of healthcare teams to develop and maintain team situational awareness, or a shared perception, comprehension and subsequent projection of what is going on in complex and changing clinical environments, has been described as crucial for patient safety. 23 24 Through participation and working together, 25 teams gain an understanding of the roles, skills and competencies of others to demonstrate ‘collective competence’, 26 27 which is critical for healthcare delivery, 28 29 and existing research on locums suggests a need for better integration into teams to improve quality and safety. 30 31

Context matters for patient safety and quality improvement, 32 33 yet the limited evidence 17 relating to locums practice is largely ‘acontextual’ and tends to ignore the role of the organisation in the integration of temporary staff, focusing instead on the potential risks locums present as individual clinicians, 17 30 which is perhaps unsurprising given the liminal space locums occupy. In the UK, responsibility for the quality and safety of healthcare services is shared primarily between organisations and the individual professionals working within them. 34 Organisations are responsible for creating systems and environments that promote and protect clinical governance and enable all doctors to meet their professional obligations, while doctors are expected to participate in the systems and processes put in place by regulators and organisations to protect and improve patient care. 35 However, NHS trusts and primary care organisations procure the services of locum doctors without assuming the responsibilities normally associated with an employer–employee relationship 30 and locums often struggle to participate in teams and governance systems that were designed for doctors working in conventional employment relationships. 18 36

There is longstanding debate about the role of individual accountability in patient safety and how responsibility is distributed between organisations and individuals. 37 A systems approach reasons that adverse events are likely to occur as a result of system failures rather than individual failures, 38 and patients are protected from mistakes by well designed systems and environments that promote safety cultures. 39 But locums are often positioned at the periphery of these systems, 30 and doctors who are new to and also peripheral to organisations, and organisations who are inexperienced with and unsupportive of locums are unlikely to be able to perform optimally. 40

The aim of this research was to provide evidence on how locum working arrangements impact quality and safety and the implications of locum working for patients, locums and health service organisations in primary and secondary care in the English NHS. Locum doctors are an essential and growing part of the healthcare workforce 1 who have been largely ignored in healthcare workforce research. This research addresses a gap in the empirical evidence base on how locum doctor working arrangements affect quality and safety, and provides, for the first time, an in-depth exploration that includes perspectives from patients, locums and the people they work with.

Study design and setting

A qualitative semi-structured interview and focus group study was conducted with locums, people working with locums, and patients with experience of being treated by locums. Participants were purposively sampled through 11 organisations, including NHS trusts, primary care practices, statutory NHS bodies and locum agencies. Locum doctor participants were recruited through these organisations, locum recruitment agencies and networks. We used purposive, snowball and convenience sampling, drawing on intelligence from stakeholders, including our project advisory group, to identify and recruit organisations and participants. Patient participants were recruited through patient and contributor forums. The forum involved active partnership between patients and researchers in the research process to develop research which is relevant and useful to patient and public needs. Participant demographics were monitored to ensure representation across a broad range of roles in primary and secondary care and to increase diversity in terms of gender and ethnicity (see table 1 ).

• View inline

Characteristics of study participants

Data collection

Three semi-structured interview and focus group guides were developed for use with locums, people working with locums and patients with experience of being treated by locums (as shown in online supplemental files 1-3 ). Our previous review of the literature relating to quality and safety and locum work 17 informed the schedules as well as the initial coding and thematic development. Schedules were also refined and informed by our patient and public involvement (PPI) forum and our project advisory group. Each schedule was intended to explore locum doctor working arrangements with a particular focus on understanding how locum doctor working may affect the safety and quality of care and what strategies or systems organisations and individuals used to assure or improve quality and safety. The topic guides for locums and people working with locums also covered governance and support, the impact of the COVID pandemic and policies and initiatives used to support locums.

Supplemental material

Interviews and focus groups were transcribed verbatim by a professional transcription company and organised into codes and themes using the software package NVivo. 41 Reflexive thematic analysis (RTA) 42 was used and involved familiarisation with the data by reading and re-reading the transcripts and field notes; coding the dataset and collating all relevant data extracts; generating initial themes by examining the codes and collated data to identify significant broader patterns of meaning across the dataset; reviewing themes by questioning whether themes answered the research question and told a convincing story of the data and combining, splitting and discarding themes as necessary; defining and naming themes by developing a detailed analysis of each theme; and finally the analytical write up which positioned the analysis in relation to existing literature. 43 RTA acknowledges the active role of researchers in knowledge production and the researcher’s subjectivity as the analytic resource. 42 RTA recognises interpretive variability between researchers based on differences in their knowledge and skills, theoretical assumptions and differences in how they responded to the dataset is acknowledged and expected. 42 The research team worked reflexively discussing their personal biases and their potential impact on the research at regular meetings throughout the data collection and analysis period. Our PPI forum were also involved in data collection and analysis, and offered a form of triangulation to enhance rigour, challenge and alternative interpretations of the findings. 44 Analysis adopted a constructionist epistemology, in that while we acknowledged the importance of recurrence in generating themes, meaning and meaningfulness were the central criteria in the coding process. 42

After themes were developed, an abductive approach was taken to position findings against a background of existing theory and knowledge. 17 30 This provided a way of constructing empirically based theorisations without confining theory to predefined concepts. 45 This approach integrated inductive data-driven coding with deductive theory-driven interpretation; aiming to find a middle ground between inductive and deductive methods and the most logical solution and useful explanation for phenomena. 45

We conducted 130 interviews with 88 participants who worked in healthcare and 42 patients took part in focus groups and one-to-one interviews. Participants included locums, permanently employed doctors; nurses and other health professionals; medical directors/clinical leaders; responsible officers (ROs are accountable for local clinical governance processes and focus on the performance of doctors) and appraisers; leads for medical staffing and clinical governance and practice managers (see table 2 ). Three experienced qualitative researchers (JF, GS and KW) and two members of the PPI forum (MM and MS) carried out five focus groups with 30 patients, and JF and GS carried out 12 one-to-one interviews. Data were collected between March 2021 and April 2022 during the COVID pandemic using video conferencing software (n=126) or over the phone (n=4) at a time convenient to participants. Interviews and focus groups ranged in length from 23 to 171 min, with the average interview being 59 min.

Healthcare organisations and participant roles

Thematic framework

Our findings are presented under five broad and interrelated themes that examine how locum work relates to and impacts quality and safety: ‘familiarity’ with an organisation and its patients and staff; ‘balance and stability’ in services with lots of locums; ‘discrimination and exclusion’ towards locums and their effects; ‘defensive practice’ by locums; and the positioning of locums outside clinical governance arrangements.

Familiarity: knowing who, where and how

Locums described often working in unfamiliar environments, sometimes with minimal induction and varying levels of support. Unfamiliarity, lack of access to or other restrictions on computer systems, policies, procedures and buildings meant that locums were not always able to do their job safely, productively or effectively.

That’s probably the biggest sort of safety aspect that sticks in my mind, is that it is unbelievably frustrating to have to learn a whole new set of patients from day to day … when I was signed up to four different hospitals, plus the locum agencies, I very quickly realised that not only is it the fact that you don’t know the patients from day to day, if you’re chopping and changing site the whole time, then store cupboards are laid out differently, ways of contacting relevant staff members are different, you’ve got to recognise what code to put in to bleep someone that’s different at every single site. (Interview 23, locum, secondary care)

Locum working sometimes created extra work for permanent staff who were responsible for inducting, training and supervising locums. The amount of additional workload was dependent on contextual factors, such as the experience of the locum, organisational support and length of placement, access to systems and what terms and conditions locums or organisations had negotiated. Locum reliance on permanent staff meant that care could be delayed, partially completed or not completed at all, which sometimes caused resentment.

Some of the things that we don’t … like, for example, procedures of limited clinical value that we don’t refer in for, they won’t know about those in our areas … So they’ll do referrals that we then will get pulled on. They’ll maybe prescribe medications that are not first line medications within our own formulary. So we see quite a bit of that, you know, there’s quite a lot of tidying up to be done afterwards or work. They generate that. So whilst we meet the patient numbers, they create a lot of work for the rest of the team. (Interview 3, practice manager, primary care)

Locums mitigated risks related to working in unfamiliar environments by avoiding organisations considered chaotic or unsafe, working below their grade to avoid having responsibility in unfamiliar organisations where they may not be supported or included in the team or working in a limited number of organisations to increase familiarity.

Most locums take jobs, locum work below their grade. So a person who’s at a registrar level would take a locum work as an SHO (senior house officer), because they don't know the trust that well. (Interview 55, locum, secondary care)

However, lack of familiarity and discontinuity could at times be beneficial for patients and organisations as fresh perspectives offered by locums led to different routes of treatment or management, and could alter organisational cultures or practices.

So that [locum] doctor, through that line of questioning and not having any sort of prior history … ordered the right tests and didn’t feel constrained in that practice about what tests that they could order. And someone subsequently … because when you get referred to hospital, the consultant said that that doctor was very much on the ball. And, of course, that’s a change to lifelong medication. And literally within a month of the medication kicking in, it transformed my life. (Focus group A, patient 1)

Balance and stability

The balance between locum and permanent staff had implications for quality and safety, organisational leadership, long-term planning and governance. Locums were often employed to deliver immediate services and consequently were less likely to be involved in team and organisational development. Locums recognised that having ‘an NHS run by locums’ was detrimental to quality and safety, and some avoided organisations that were locum dependent for this reason. Well functioning established teams were regarded as better able to incorporate a small number of locums without being significantly impacted.

Locum work, my view on it is they’re there to fill a gap. They shouldn’t be relied upon to deliver a service Monday to Friday, day in, day out, week in, week out. And unfortunately my trust see it as that, though, that’s my worry that they feel they’re not just plugging a gap, they’re almost as a workforce … (Interview 84, lead GP, primary and secondary care)

Departments that were disproportionately locum dependent were often perceived to lack clinical leadership and direction. An absence of consistent medical leadership meant that quality improvement was slower or less likely to happen, and trusting relationships between staff were harder to establish.

If you get a department that is disproportionately locum dependent, then it stagnates, it doesn't progress. Things like implementation of new NICE guidance, for example, that sort of thing tends not to happen or happen less well, less quickly. (Interview 30, responsible officer, secondary care)

Discrimination and exclusion

Most locums described negative behaviours and attitudes from staff and some patients, which impacted their involvement, inclusion and experiences in organisations. Negative attitudes and behaviours towards locums could affect turnover, locum well-being, team dynamics and potentially patient safety. Perceived disparities between pay, workload, competence and organisational and team commitment between locums and permanent staff could be sources of resentment and influenced how locums were treated and viewed. This compromised staff communication and reduced the sharing of important patient information.

I guess like any temporary post really, you struggle to invest in them, don't necessarily see them as being part of the team. Not very positive about them, particularly junior staff, particularly in the acute trusts. We'd have locums refusing to come back because of the treatment of the midwives. (Interview 86, clinical lead, secondary care)

Negative perceptions of competency and safety meant that locums were often stigmatised, marginalised and excluded. The identity of locum intersected and overlapped with other identities and was described as ‘layering up’ with ethnicity and gender to further exacerbate discrimination.

Oh, doctors coming over from Germany. There was one locum … that administered a dose of something and the patient died, and then there’s this whole layer of extra negativity attached to locum doctors in general because of what one doctor did, and that doctor happened to be someone from a different ethnicity … As a UK born and qualified doctor I can see that those overseas get it but I can also see that I have experienced that as well. So yeah, it can layer up with the whole locum thing. (Interview 59, locum GP, primary care)

A sense of othering and being seen as less was particularly evident during the COVID pandemic when resources were limited. Some locums described how they were not afforded the same protections as permanent members of staff and were sometimes expected to take on riskier work.

I’ve worked in another practice where, because they live on locums and they live on ad hoc locums, you’re a piece of dirt under the shoe. You don’t get gloves, you didn’t have aprons, you didn’t have a face visor, you didn’t have safety specs, you have to ask for a mask. Not only are you not treated as a service provider, you’re not treated as a colleague, someone with knowledge. (Interview 44, locum GP)

Defensive practice by locums

Locums recognised that they were likely to be scapegoated if things went wrong, and some locums described being more likely to practice defensively. Defensive practice has been defined as deviation from standard practice to avoid litigation, complaints or criticism. 46 Participants reported instances of defensive practice which involved providing services (eg, tests, referrals) or avoiding high-risk decisions, usually to reduce the risk of adverse outcomes such as patient complaints or potential termination of contract at short notice. Locums described practicing defensively because they were attempting to practice as safely as possible in complex unfamiliar environments where they were professionally isolated and perceived negatively. Permanent members of staff could perceive that locums practiced defensively because they lacked confidence in their abilities. The diversion of resources away from more clinically relevant activities placed additional burden on teams, who were already facing significant workload challenges.

Being risk averse and practising defensive medicine usually means more tests, more referrals, whereas holding risk tends to be disadvantageous for you as a locum because what’s the benefit to you of not doing that. You’re benefiting the system by rationing resource, the patient won’t thank you. (Interview 35, locum GP)

Locums described avoiding making decisions when risks to employment or medical licenses were perceived as high. Locums felt they were more vulnerable to criticisms of their clinical competence and disempowered to make decisions. Others felt that some locums were simply avoiding work and evaded responsibility for patients by pushing work onto others or into the future.

You don’t interfere, very simple. Over time locums have learned that if you interfere, if you participate in the team, you participate in patient care, [and this] is when you get into trouble … Well most of the locums that I know will just say, okay, there’s already somebody else who’s made a decision, it’s not my job to make a decision, I just follow through. If things go wrong, call the senior person and be done with it, that’s the end of my role. Actually doing something to protect a patient is not important for a locum because the risk is too high. (Interview 55, locum, secondary care)

Locums fall outside clinical governance arrangements

Governance practices in relation to locums varied widely and were not generally regarded as being as robust in comparison to permanently employed doctors. Responsibility for involving locum doctors in performance feedback, supervision, educational opportunities, appraisal and quality improvement was unclear. While some organisations included locums in their governance activities, others regarded locum work as transactional; where the locum was there to provide a finite service and the organisation assumed no responsibilities for their performance, development or oversight. There were concerns that governance structures were modelled on and designed for permanently employed doctors and did not work for locums. When deficits in performance were undetected or unaddressed, doctor performance and patient safety could be jeopardised.

I think it’s a remote world. It’s like a cloud, you know, it’s like the cloud. We talk about the cloud when it comes to storing information. And I think locum world is a bit like that … And I don’t know the doctors anywhere like as much as I did when I was an RO in the NHS, I knew them all personally. If I used to have a problem, I used to get them in my office there and then, chat it all through, sort it. Can’t do that in locum world, it might take me four days to get hold of the doctor, some of them won’t respond immediately … They don’t know me and I don’t know them. (Interview 51, responsible officer, locum agency)

The absence of typical recruitment processes (involving meeting a doctor, carrying out an interview and following up on references) meant that healthcare organisations were reliant on partial information from locum agencies, which made it difficult to determine competency, scope of practice and suitability for a role. However, staff shortages and a requirement to meet safe staffing ratios meant that organisational leaders had little recourse of action if they were unsure about a doctor’s capability, which caused anxiety and frustration. This suggests that the provision of healthcare superseded ensuring safety standards and necessitated accepting one of two objectionable alternatives; accepting gaps in staffing that may jeopardise patient safety or accepting unknown doctors; each of which may compromise patient safety.

If a locum turns up and I have serious doubts about their ability to do the job to the required standard, I don’t have any recourse … And therefore I’m in a position where either I accept this locum or I don’t. There’s not much in the way of middle ground. Not accepting them is a really unpalatable choice because if I say look, I’m sorry, I don’t think you’re up to this, I think you should go home, that leaves me with a gap. (Interview 30, consultant and responsible officer, secondary care)

Similar governance and information sharing problems were described by locum agencies and NHS organisations; both described difficulties in gathering and sharing feedback. When concerns were raised, participants were often uncertain as to what happened to the information they provided and whether it was shared or acted on. Locums often did not get to hear about concerns raised about them, meaning learning opportunities were missed.

It would give you more confidence if you heard back. And sometimes I'll pick up the phone and you try to do the best you can to make sure this information gets passed on. But I just have this nagging doubt that I'm not always convinced it does. (Interview 30, responsible officer, secondary care)

There was also a perception from some locum agency responsible officers that while most locum doctors were excellent, there were some locums who were isolated and in need of organisational and professional support.

You have to accept that whilst within the agency world, 80 per cent of the doctors we place are excellent, and have no problems, and do a great job, perhaps 20 per cent are those that have shaken down to that 20 per cent in the agency world, because they’ve not succeeded in the NHS, they’ve not got a substantive place, they are lost souls. And they are less able to cope with the vicissitudes of busy clinical life and professional life within a large organisation such as the NHS. (Interview 47, responsible officer, locum agency)

Our findings provide some profound and concerning insights for patient safety and quality of care. The ways in which locums were recruited, inducted, deployed and integrated, and supported by organisations undoubtedly affected quality and safety. Our findings indicate that regardless of their level of experience, it was unlikely that locum doctors would be able to function optimally in unfamiliar environments; and organisations who had poor supportive infrastructure and governance mechanisms for locums were less likely to deliver high-quality safe services.

Locums were often regarded as organisational outsiders—positioned at the periphery of the team and the organisation. The implications of transience and peripheral participation were weaker relationships with organisations, teams, peers and patients, leading some to suggest locum working is better suited to experienced doctors. 47 Consistent with previous research, 48 frequent variation in process, systems and equipment, combined with disruption in relationships and a lack of mutual awareness of team skills and competencies, decreased collective competence, placed additional burden on the wider healthcare team and reduced patient safety. As others have found in research on safe staffing and nursing, 49 temporary staff are not effective substitutes for staff who regularly work in the organisation. Safe medical staffing is not just achieved by filling rota gaps, but also team composition and doctors’ familiarity with the team and organisation must be taken into account. Regulatory agencies should consider locum usage in their inspections and perhaps be particularly concerned when organisations have ‘services run on locums’.

Our research found, as others have, 18 that organisations and doctors sometimes struggled to meet their governance obligations and that governance activities differed based on contractual status and organisational policies and norms, with systems being less robust for locums. This research has highlighted that much still needs to be done to develop governance systems that promote and protect the interests of patients and create an environment which supports locum doctors in meeting their professional obligations.

More positively, locum doctors are a potentially valuable source of information about safety concerns, faulty systems or poor conduct. 50 Locums move between organisations, have broad systems knowledge and are perhaps better placed to identify some quality and safety issues than permanent doctors. However, findings indicate opportunities for shared learning were often missed. Locums recognised their precarity and vulnerability when offering second opinions, sharing improvement ideas or voicing safety concerns; meaning opinions were not always offered and concerns were not always raised. Failure to voice concerns is a persistent problem in healthcare, 51 and locums may be even less inclined to offer potentially valuable information about safety concerns because of their perceptions of unsupportive organisational climates.

Our findings shed light on how temporary doctors fit into the enduring debate 37 around how responsibility between organisational systems and individual professionals is distributed. Locums appear to represent a subsection of the medical profession for whom the wider paradigm shift from a focus on individual blame to a systems approach 52 appeared not to have been made. Locums were often not regarded as a part of the organisation, and therefore the system, and not afforded the same protections as permanent staff when things went wrong. Blaming locums when things go wrong and punishing or sanctioning individuals who make errors in contexts that were not designed to incorporate temporary workers may divert attention from understanding inadequately designed, poorly functioning systems, or indeed the individual practice of other doctors. While we should take into account systemic factors that impede locums from performing safely, we should expect high standards of healthcare professionals, be cognisant of individual agency and recognise the distinction between blaming someone and holding them responsible. 53

Strengths and limitations

This large qualitative study explores locum working and quality and safety in an under-researched, yet growing area of the medical workforce. However, sites were all based in England, which means caution should be taken when extrapolating findings. Similar research in other countries and contexts to understand more about locum doctor working and quality and safety is therefore important. It is possible that our sample may have been skewed towards locums, healthcare professionals and patients who had more negative perceptions and experiences, although accounts resonate with previous research 30 and patient perspectives were generally positive. Our data were collected during the COVID pandemic, which may have affected findings as there was a reduction in locum working during that time 10 11 ; it also meant we were unable to carry out observations, which would have strengthened our findings and mitigated some of the inherent limitations of interviews, such as recall bias. We used both one-to-one interviews and focus groups in data collection. Although flexibility in data collection meant that participants had the option to take part in an interview or a focus group, these methods are used for different reasons and produce different data. There may have been differences in what participants disclosed depending on the method

Our findings show that the way in which doctors who worked on a temporary basis were integrated into organisations posed some significant challenges and opportunities for patient safety and quality of care, and that both organisations and locums had a part to play in improvement. Doctors working as locums are a heterogeneous group with differing backgrounds, experiences, skills and capabilities that likely reflect the variability seen in the wider population of doctors. Locums are working in the same pressured and imperfect systems as other health workers; it is vital that systemic problems are not mistaken for problems about individuals and important to recognise that a locum is not a type of doctor but a way of working. Our findings are a call to action for organisations to take stock of how they engage, support and work with locums, and asks both locums and organisations to reflect on whether their practices support a collective approach to patient safety and quality of care.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the Health Research Authority North West—Haydock Research Ethics Committee 20/NW/0386. Participants gave informed consent to participate in the study before taking part.

• General Medical Council
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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3
• Data supplement 4

Twitter @janefergo, @@kieran_walshe

Contributors JF, KW, DA, TA and EK conceived the study. Recruitment was led by JF and supported by GS. JF, GS and KW conducted the interviews, reviewed and analysed the transcripts, and JF wrote the first version of the manuscript. Two members of the patient and public involvement (PPI) forum also assisted with focus groups. JF conducted data analysis with input from KW and GS, the PPI forum, and review by all authors. JF and KW were involved in initial critical review and revision of the manuscript, followed by all authors. All authors read and approved the final version of the manuscript. JF is the guarantor.

Funding This study was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR128349), and the NIHR Greater Manchester Patient Safety Research Collaboration (PSRC). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Editorial Locums: threat or opportunity Richard Lilford BMJ Quality & Safety 2024; - Published Online First: 16 Apr 2024. doi: 10.1136/bmjqs-2023-016951

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Enhancing Semantic Understanding by Bridging Topic Modeling and Thematic Analysis: An Empirical Study on Self-Help Twitter Corpus and In-Depth Interviews

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Bridging data science with social science is useful for social scientists to develop a more transparent approach to their data analysis and enhance the rigor and credibility of qualitative research. This study aims to offer the potential of an iterative process which allows to discover some patterns through exploratory analysis of big Twitter data drawn from four influential self-help gurus’ accounts and grounding these patterns into smaller but richer interview data based on 24 in-depth interviews conducted among self-help consumers. Going in between the findings from topic modeling and thematic analysis helped triangulate the analysis of the same research question from multiple data sources and from different methodological perspectives.

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Urhan, C. (2024). Enhancing Semantic Understanding by Bridging Topic Modeling and Thematic Analysis: An Empirical Study on Self-Help Twitter Corpus and In-Depth Interviews. In: Araújo, S., Aguiar, M., Ermakova, L. (eds) Digital Humanities Looking at the World . Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-48941-9_5

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A Review of the Quality Indicators of Rigor in Qualitative Research

Jessica l. johnson.

a William Carey University School of Pharmacy, Biloxi, Mississippi

Donna Adkins

Sheila chauvin.

b Louisiana State University, School of Medicine, New Orleans, Louisiana

Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A research question must be clear and focused and supported by a strong conceptual framework, both of which contribute to the selection of appropriate research methods that enhance trustworthiness and minimize researcher bias inherent in qualitative methodologies. Qualitative data collection and analyses are often modified through an iterative approach to answering the research question. Researcher reflexivity, essentially a researcher’s insight into their own biases and rationale for decision-making as the study progresses, is critical to rigor. This article reviews common standards of rigor, quality scholarship criteria, and best practices for qualitative research from design through dissemination.

INTRODUCTION

Within the past 20 years, qualitative research in health professions education has increased significantly, both in practice and publication. Today, one can pick up most any issue of a wide variety of health professions education journals and find at least one article that includes some type of qualitative research, whether a full study or the inclusion of a qualitative component within a quantitative or mixed methods study. Simultaneously, there have been recurrent calls for enhancing rigor and quality in qualitative research.

As members of the academic community, we share responsibility for ensuring rigor in qualitative research, whether as researchers who design and implement, manuscript reviewers who critique, colleagues who discuss and learn from each other, or scholarly teachers who draw upon results to enhance and innovate education. Therefore, the purpose of this article is to summarize standards of rigor and suggested best practices for designing, conducting, and reporting high-quality qualitative research. To begin, Denzin and Lincoln’s definition of qualitative research, a long-standing cornerstone in the field, provides a useful foundation for summarizing quality standards and best practices:

Qualitative research involves the studied use and collection of a variety of empirical materials – case study; personal experience; introspection; life story; interview; artifacts; cultural texts and productions; observational, historical, interactional, and visual texts – that describe the routine and problematic moments and meanings in individual lives. Accordingly, qualitative researchers deploy a wide range of interconnected interpretative practices, hoping always to get a better understanding of the subject matter at hand. It is understood, however, that each practice makes the world visible in a different way. Hence there is frequently a commitment to using more than one interpretative practice in any study. 1

In recent years, multiple publications have synthesized quality criteria and recommendations for use by researchers and peer reviewers alike, often in the form of checklists. 2-6 Some authors have raised concerns about the use of such checklists and adherence to strict, universal criteria because they do not afford sufficient flexibility to accommodate the diverse approaches and multiple interpretive practices often represented in qualitative studies. 7-11 They argue that a strict focus on using checklists of specific technical criteria may stifle the diversity and multiplicity of practices that are so much a part of achieving quality and rigor within the qualitative paradigm. As an alternative, some of these authors have published best practice guidelines for use by researchers and peer reviewers to achieve and assess methodological rigor and research quality. 12,13

Some journals within the field of health professions education have also established best practice guidance, as opposed to strict criteria or a checklist, for qualitative research. These have been disseminated as guiding questions or evaluation categories. In 2015, Academic Medicine produced an expanded second edition of a researcher/author manual that includes specific criteria with extensive explanations and examples. 14 Still others have disseminated best practice guidelines through a series of methodological articles within journal publications. 2

In this article, attributes of rigor and quality and suggested best practices are presented as they relate to the steps of designing, conducting, and reporting qualitative research in a step-wise approach.

BEST PRACTICES: STEP-WISE APPROACH

Step 1: identifying a research topic.

Identifying and developing a research topic is comprised of two major tasks: formulating a research question, and developing a conceptual framework to support the study. Formulating a research question is often stimulated by real-life observations, experiences, or events in the researcher’s local setting that reflect a perplexing problem begging for systematic inquiry. The research question begins as a problem statement or set of propositions that describe the relationship among certain concepts, behaviors, or experiences. Agee 15 and others 16,17 note that initial questions are usually too broad in focus and too vague regarding the specific context of the study to be answerable and researchable. Creswell reminds us that initial qualitative research questions guide inquiry, but they often change as the author’s understanding of the issue develops throughout the study. 16 Developing and refining a primary research question focused on both the phenomena of interest and the context in which it is situated is essential to research rigor and quality.

Glassick, Huber, and Maeroff identified six criteria applicable to assessing the quality of scholarship. 18,19 Now commonly referred to as the Glassick Criteria ( Table 1 ), these critical attributes outline the essential elements of any scholarly approach and serve as a general research framework for developing research questions and designing studies. The first two criteria, clear purpose and adequate preparation, are directly related to formulating effective research questions and a strong conceptual framework.

Glassick’s Criteria for Assessing the Quality of Scholarship of a Research Study 18

Generating and refining a qualitative research question requires thorough, systematic, and iterative review of the literature, and the use of those results to establish a clear context and foundation for the question and study design. Using an iterative approach, relevant concepts, principles, theories or models, and prior evidence are identified to establish what is known, and more importantly, what is not known. The iterative process contributes to forming a better research question, the criteria for which can be abbreviated by the acronym FINER, ie, f easible, i nteresting, n ovel, e thical, and r elevant, that is answerable and researchable, in terms of research focus, context specificity, and the availability of time, logistics, and resources to carry out the study. Developing a FINER research question is critical to study rigor and quality and should not be rushed, as all other aspects of research design depend on the focus and clarity of the research question(s) guiding the study. 15 Agee provides clear and worthwhile additional guidance for developing qualitative research questions. 15

Reflexivity, the idea that a researcher’s preconceptions and biases can influence decisions and actions throughout qualitative research activities, is a critical aspect of rigor even at the earliest stages of the study. A researcher’s background, beliefs, and experiences may affect any aspect of the research from choosing which specific question to investigate through determining how to present the results. Therefore, even at this early stage, the potential effect of researcher bias and any ethical considerations should be acknowledged and addressed. That is, how will the question’s influence on study design affect participants’ lives, position the researcher in relationship with others, or require specific methods for addressing potential areas of research bias and ethical considerations?

A conceptual framework is then actively constructed to provide a logical and convincing argument for the research. The framework defines and justifies the research question, the methodology selected to answer that question, and the perspectives from which interpretation of results and conclusions will be made. 5,6,20 Developing a well-integrated conceptual framework is essential to establishing a research topic based upon a thorough and integrated review of relevant literature (addressing Glassick criteria #1 and #2: clear purpose and adequate preparation). Key concepts, principles, assumptions, best practices, and theories are identified, defined, and integrated in ways that clearly demonstrate the problem statement and corresponding research question are answerable, researchable, and important to advancing thinking and practice.

Ringsted, Hodges, and Sherpbier describe three essential parts to an effective conceptual framework: theories and/or concepts and principles relevant to the phenomenon of interest; what is known and unknown from prior work, observations, and examples; and the researcher’s observations, ideas, and suppositions regarding the research problem statement and question. 21 Lingard describes four types of unknowns to pursue during literature review: what no one knows; what is not yet well understood; what controversy or conflicting results, understandings, or perspectives exist; and what are unproven assumptions. 22 In qualitative research, these unknowns are critical to achieving a well-developed conceptual framework and a corresponding rigorous study design.

Recent contributions from Ravitch and colleagues present best practices in developing frameworks for conceptual and methodological coherence within a study design, regardless of the research approach. 23,24 Their recommendations and arguments are highly relevant to qualitative research. Figure 1 reflects the primary components of a conceptual framework adapted from Ravitch and Carl 23 and how all components contribute to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice. Notice that each element of the framework interacts with and influences other elements in a dynamic and interactive process from the beginning to the end of a research project. The intersecting bidirectional arrows represent direct relationships between elements as they relate to specific aspects of a qualitative research study.

Adaptation of Ravitch and Carl’s Components of a Conceptual Framework 23

Maxwell also provides useful guidance for developing an effective conceptual framework specific to the qualitative research paradigm. 17 The 2015 second edition of the Review Criteria for Research Manuscripts 14 and work by Ravitch and colleagues 23,24 provide specific guidance for applying the conceptual framework to each stage of the research process to enhance rigor and quality. Quality criteria for assessing a study’s problem statement, conceptual framework, and research question include the following: introduction builds a logical case and provides context for the problem statement; problem statement is clear and well-articulated; conceptual framework is explicit and justified; research purpose and/or question is clearly stated; and constructs being investigated are clearly identified and presented. 14,24,25 As best practice guidelines, these criteria facilitate quality and rigor while providing sufficient flexibility in how each is achieved and demonstrated.

While a conceptual framework is important to rigor in qualitative research, Huberman and Miles caution qualitative researchers about developing and using a framework to the extent that it influences qualitative design deductively because this would violate the very principles of induction that define the qualitative research paradigm. 25 Our profession’s recent emphasis on a holistic admissions process for pharmacy students provides a reasonable example of inductive and deductive reasoning and their respective applications in qualitative and quantitative research studies. Principles of inductive reasoning are applied when a qualitative research study examines a representative group of competent pharmacy professionals to generate a theory about essential cognitive and affective skills for patient-centered care. Deductive reasoning could then be applied to design a hypothesis-driven prospective study that compares the outcomes of two cohorts of students, one group admitted using traditional criteria and one admitted based on a holistic admissions process revised to value the affective skills of applicants. Essentially, the qualitative researcher must carefully generate a conceptual framework that guides the research question and study design without allowing the conceptual framework to become so rigid as to dictate a testable hypothesis, which is the founding principle of deductive reasoning. 26

Step 2: Qualitative Study Design

The development of a strong conceptual framework facilitates selection of appropriate study methods to minimize the bias inherent in qualitative studies and help readers to trust the research and the researcher (see Glassick criteria #3 in Table 1 ). Although researchers can employ great flexibility in the selection of study methods, inclusion of best practice methods for assuring the rigor and trustworthiness of results is critical to study design. Lincoln and Guba outline four criteria for establishing the overall trustworthiness of qualitative research results: credibility, the researcher ensures and imparts to the reader supporting evidence that the results accurately represent what was studied; transferability, the researcher provides detailed contextual information such that readers can determine whether the results are applicable to their or other situations; dependability, the researcher describes the study process in sufficient detail that the work could be repeated; confirmability, the researcher ensures and communicates to the reader that the results are based on and reflective of the information gathered from the participants and not the interpretations or bias of the researcher. 27

Specific best practice methods used in the sampling and data collection processes to increase the rigor and trustworthiness of qualitative research include: clear rationale for sampling design decisions, determination of data saturation, ethics in research design, member checking, prolonged engagement with and persistent observation of study participants, and triangulation of data sources. 28

Qualitative research is focused on making sense of lived, observed phenomenon in a specific context with specifically selected individuals, rather than attempting to generalize from sample to population. Therefore, sampling design in qualitative research is not random but defined purposively to include the most appropriate participants in the most appropriate context for answering the research question. Qualitative researchers recognize that certain participants are more likely to be “rich” with data or insight than others, and therefore, more relevant and useful in achieving the research purpose and answering the question at hand. The conceptual framework contributes directly to determining sample definitions, size, and recruitment of participants. A typical best practice is purposive sampling methods, and when appropriate, convenience sampling may be justified. 29

Purposive sampling reflects intentional selection of research participants to optimize data sources for answering the research question. For example, the research question may be best answered by persons who have particular experience (critical case sampling) or certain expertise (key informant sampling). Similarly, additional participants may be referred for participation by active participants (snowball sampling) or may be selected to represent either similar or opposing viewpoints (confirming or disconfirming samples). Again, the process of developing and using a strong conceptual framework to guide and justify methodological decisions, in this case defining and establishing the study sample, is critical to rigor and quality. 30 Convenience sampling, using the most accessible research participants, is the least rigorous approach to defining a study sample and may result in low accuracy, poor representativeness, low credibility, and lack of transferability of study results.

Qualitative studies typically reflect designs in which data collection and analysis are done concurrently, with results of ongoing analysis informing continuing data collection. Determination of a final sample size is largely based on having sufficient opportunity to collect relevant data until new information is no longer emerging from data collection, new coding is not feasible, and/or no new themes are emerging; that is, reaching data saturation , a common standard of rigor for data collection in qualitative studies . Thus, accurately predicting a sample size during the planning phases of qualitative research can be challenging. 30 Care should be taken that sufficient quantity (think thick description) and quality (think rich description) of data have been collected prior to concluding that data saturation has been achieved. A poor decision regarding sample size is a direct consequence of sampling strategy and quality of data generated, which leaves the researcher unable to fully answer the research question in sufficient depth. 30

Though data saturation is probably the most common terminology used to describe the achievement of sufficient sample size, it does not apply to all study designs. For example, one could argue that in some approaches to qualitative research, data collection could continue infinitely if the event continues infinitely. In education, we often anecdotally observe variations in the personality and structure of a class of students, and as generations of students continue to evolve with time, so too would the data generated from observing each successive class. In such situations, data saturation might never be achieved. Conversely, the number of participants available for inclusion in a sample may be small and some risk of not reaching data saturation may be unavoidable. Thus, the idea of fully achieving data saturation may be unrealistic when applied to some populations or research questions. In other instances, attrition and factors related to time and resources may contribute to not reaching data saturation within the limits of the study. By being transparent in the process and reporting of results when saturation may not have been possible, the resulting data may still contribute to the field and to further inquiry. Replication of the study using other samples and conducting additional types of follow-up studies are other options for better understanding the research phenomenon at hand. 31

In addition to defining the sample and selecting participants, other considerations related to sampling bias may impact the quantity and quality of data generated and therefore the quality of the study result. These include: methods of recruiting, procedures for informed consent, timing of the interviews in relation to experience or emotion, procedures for ensuring participant anonymity/confidentiality, interview setting, and methods of recording/transcribing the data. Any of these factors could potentially change the nature of the relationship between the researcher and the study participants and influence the trustworthiness of data collected or the study result. Thus, ongoing application of previously mentioned researcher reflexivity is critical to the rigor of the study and quality of sampling. 29,30

Common qualitative data collection methods used in health professions education include interview, direct observation methods, and textual/document analysis. Given the unique and often highly sensitive nature of data being collected by the researcher, trustworthiness is an essential component of the researcher-participant relationship. Ethical conduct refers to how moral principles and values are part of the research process. Participants’ perceptions of ethical conduct are fundamental to a relationship likely to generate high quality data. During each step of the research process, care must be taken to protect the confidentiality of participants and shield them from harm relating to issues of respect and dignity. Researchers must be respectful of the participants’ contributions and quotes, and results must be reported truthfully and honestly. 8

Interview methods range from highly structured to increase dependability or completely open-ended to allow for interviewers to clarify a participant’s response for increased credibility and confirmability. Regardless, interview protocols and structure are often modified or refined, based on concurrent data collection and analysis processes to support or refute preliminary interpretations and refine focus and continuing inquiry. Researcher reflexivity, or acknowledgement of researcher bias, is absolutely critical to the credibility and trustworthiness of data collection and analysis in such study designs. 32

Interviews should be recorded and transcribed verbatim prior to coding and analysis. 28 Member checking, a common standard of rigor, is a practice to increase study credibility and confirmability that involves asking a research subject to verify the transcription of an interview. 1,16,28 The research subject is asked to verify the completeness and accuracy of an interview transcript to ensure the transcript truthfully reflects the meaning and intent of the subject’s contribution.

Prolonged engagement involves the researcher gaining familiarity and understanding of the culture and context surrounding the persons or situations being studied. This strategy supports reflexivity, allowing the researcher to determine how they themselves may be a source of bias during the data collection process by altering the nature of how individuals behave or interact with others in the presence of the researcher. Facial expressions, spoken language, body language, style of dress, age, race, gender, social status, culture, and the researcher’s relationship with the participants may potentially influence either participants’ responses or how the researcher interprets those responses. 33 “Fitting in” by demonstrating an appreciation and understanding of the cultural norms of the population being studied potentially allows the researcher to obtain more open and honest responses from participants. However, if the research participants or topic are too familiar or personal, this may also influence data collection or analysis and interpretation of the results. 33 The possible applications of this section to faculty research with student participants in the context of pharmacy education are obvious, and researcher reflexivity is critical to rigor.

Some researchers using observational methods adopt a strategy of direct field observation, while others play partial or full participant roles in the activity being observed. In both observation scenarios, it is impossible to separate the researcher from the environment, and researcher reflexivity is essential. The pros and cons of observation approach, relative to the research question and study purpose, should be evaluated by the researcher, and the justification for the observational strategy selected should be made clear. 34 Regardless of the researcher’s degree of visibility to the study participants, persistent observation of the targeted sample is critical to the confirmability standard and to achieving data saturation. That is, study conclusions must be clearly grounded in persistent phenomena witnessed during the study, rather than on a fluke event. 28

Researchers acknowledge that observational methodologies are limited by the reality that the researcher carries a bias in determining what is observed, what is recorded, how it is recorded, and how it is transcribed for analysis. A study’s conceptual framework is critical to achieving rigor and quality and provides guidance in developing predetermined notions or plans for what to observe, how to record, and how to minimize the influence of potential bias. 34 Researcher notes should be recorded as soon as possible after the observation event to optimize accuracy. The more detailed and complete the notes, the more accurate and useful they can be in data analysis or in auditing processes for enhancing rigor in the interpretation phase of the study. 34

Triangulation is among the common standards of rigor applied within the qualitative research paradigm. Data triangulation is used to identify convergence of data obtained through multiple data sources and methods (eg, observation field notes and interview transcripts) to avoid or minimize error or bias and optimize accuracy in data collection and analysis processes. 33,35,36

Again, researcher practice in reflexivity throughout research processes is integral to rigor in study design and implementation. Researchers must demonstrate attention to appropriate methods and reflective critique, which are represented in both core elements of the conceptual framework ( Figure 1 ) and Glassick criteria ( Table 1 ). In so doing, the researcher will be well-prepared to justify sampling design and data collection decisions to manuscript reviewers and, ultimately, readers.

Step 3: Data Analysis

In many qualitative studies, data collection runs concurrently with data analysis. Specific standards of rigor are commonly used to ensure trustworthiness and integrity within the data analysis process, including use of computer software, peer review, audit trail, triangulation, and negative case analysis.

Management and analyses of qualitative data from written text, observational field notes, and interview transcriptions may be accomplished using manual methods or the assistance of computer software applications for coding and analysis. When managing very large data sets or complex study designs, computer software can be very helpful to assist researchers in coding, sorting, organizing, and weighting data elements. Software applications can facilitate ease in calculating semi-quantitative descriptive statistics, such as counts of specific events, that can be used as evidence that the researcher’s analysis is based on a representative majority of data collected ( inclusivism ) rather than focusing on selected rarities ( anecdotalism ). Using software to code data can also make it easier to identify deviant cases, detect coding errors, and estimate interrater reliability among multiple coders. 37 While such software helps to manage data, the actual analyses and interpretation still reside with the researcher.

Peer review, another common standard of rigor, is a process by which researchers invite an independent third-party researcher to analyze a detailed audit trail maintained by the study author. The audit trail methodically describes the step-by-step processes and decision-making throughout the study. Review of this audit trail occurs prior to manuscript development and enhances study confirmability. 1,16 The peer reviewer offers a critique of the study methods and validation of the conclusions drawn by the author as a thorough check on researcher bias.

Triangulation also plays a role in data analysis, as the term can also be used to describe how multiple sources of data can be used to confirm or refute interpretation, assertions, themes, and study conclusions. If a theme or theory can be arrived at and validated using multiple sources of data, the result of the study has greater credibility and confirmability. 16,33,36 Should any competing or controversial theories emerge during data collection or analysis, it is vital to the credibility and trustworthiness of the study that the author disclose and explore those negative cases. Negative case analysis refers to actively seeking out and scrutinizing data that do not fit or support the researcher’s interpretation of the data. 16

The use of best practices applying to data collection and data analysis facilitates the full examination of data relative to the study purpose and research question and helps to prevent premature closure of the study. Rather than stopping at the initial identification of literal, first-level assertion statements and themes, authors must progress to interpreting how results relate to, revise, or expand the conceptual framework, or offer an improved theory or model for explaining the study phenomenon of interest. Closing the loop on data collection is critical and is achieved when thorough and valid analysis can be linked back to the conceptual framework, as addressed in the next section.

Step 4: Drawing Valid Conclusions

Lingard and Kennedy 38 succinctly state that the purpose of qualitative research is to deepen one’s understanding of specific perspectives, observations, experiences, or events evidenced through the behaviors or products of individuals and groups as they are situated in specific contexts or circumstances. Conclusions generated from study results should enhance the conceptual framework, or contribute to a new theory or model development, and are most often situated within the discussion and conclusion sections of a manuscript.

The discussion section should include interpretation of the results and recommendations for practice. Interpretations should go beyond first-level results or literal description of observed behaviors, patterns, and themes from analysis. The author’s challenge is to provide a complete and thorough examination and explanation of how specific results relate to each other, contribute to answering the research question, and achieve the primary purpose of the research endeavor. The discussion should “close the loop” by integrating study results and analysis with the original conceptual framework. The discussion section should also provide a parsimonious narrative or graphical explanation and interpretation of study results that enhances understanding of the targeted phenomena.

The conclusion section should provide an overall picture or synopsis of the study, including its important and unique contributions to the field from the perspective of both conceptual and practical significance. The conclusion should also include personal and theoretical perspectives and future directions for research. Together, the discussion and conclusion should include responses to the larger questions of the study’s contributions, such as: So what? Why do these results matter? What next?

The strength of conclusions is dependent upon the extent to which standards of rigor and best practices were demonstrated in design, data collection, data analysis, and interpretation, as described in previous sections of this article. 4,12,17,23,24 Quality and rigor expectations for drawing valid conclusions and generating new theories are reflected in the following essential features of rigor and quality, which include: “Close the loop” to clearly link research questions, study design, data collection and analysis, and interpretation of results. Reflect effective integration of the study results with the conceptual framework and explain results in ways that relate, support, elaborate, and/or challenge conclusions of prior scholarship. Descriptions of new or enhanced frameworks or models are clear and effectively grounded in the study results and conclusions. Practical or theoretical implications are effectively discussed, including guidance for future studies. Limitations and issues of reflexivity and ethics are clearly and explicitly described, including references to actions taken to address these areas. 3,4,12,14

Step 5: Reporting Research Results

Key to quality reporting of qualitative research results are clarity, organization, completeness, accuracy, and conciseness in communicating the results to the reader of the research manuscript. O’Brien and others 4 proposed a standardized framework specifically for reporting qualitative studies known as the Standards for Reporting Qualitative Research (SRQR, Table 2 ). This framework provides detailed explanations of what should be reported in each of 21 sections of a qualitative research manuscript. While the SRQR does not explicitly mention a conceptual framework, the descriptions and table footnote clarification for the introduction and problem statement reflect the essential elements and focus of a conceptual framework. Ultimately, readers of published work determine levels of credibility, trustworthiness, and the like. A manuscript reviewer, the first reader of a study report, has the responsibility and privilege of providing critique and guidance to authors regarding achievement of quality criteria, execution and reporting of standards of rigor, and the extent to which meaningful contributions to thinking and practice in the field are presented. 13,39

An Adaptation of the 21 Elements of O’Brien and Colleagues’ Standards for Reporting Qualitative Research (SRQR) 4

Authors must avoid language heavy with connotations or adjectives that insert the researcher’s opinion into the database or manuscript. 14,40 The researcher should be as neutral and objective as possible in interpreting data and in presenting results. Thick and rich descriptions, where robust descriptive language is used to provide sufficient contextual information, enable the reader to determine credibility, transferability, dependability, and confirmability .

The process of demonstrating the credibility of research is rooted in honest and transparent reporting of how biases and other possible confounders were identified and addressed throughout study processes. Such reporting, first described within the study’s conceptual framework, should be revisited in reporting the work. Confounders may include the researcher’s training and previous experiences, personal connections to the background theory, access to the study population, and funding sources. These elements and processes are best represented in Glassick’s criteria for effective presentation and reflective critique ( Table 1 , criteria 5 and 6). Transferability is communicated, in part, through description of sampling factors such as: geographical location of the study, number and characteristics of participants, and the timeframe of data collection and analysis. 40 Such descriptions also contribute to the credibility of the results and readers’ determination of transfer to their and other contexts. To ensure dependability, the research method must be reported in detail such that the reader can determine proper research practices have been followed and that future researchers can repeat the study. 40 The confirmability of the results is influenced by reducing or at a minimum explaining any researcher influence on the result by applying and meeting standards of rigor such as member checking, triangulation, and peer review. 29,33

In qualitative studies, the researcher is often the primary instrument for data collection. Any researcher biases not adequately addressed or errors in judgement can affect the quality of data and subsequent research results. 33 Thus, due to the creative interpretative and contextually bound nature of qualitative studies, the application of standards of rigor and adherence to systematic processes well-documented in an audit trail are essential. The application of rigor and quality criteria extend beyond the researcher and are also important to effective peer review processes within a study and for scholarly dissemination. The goal of rigor in qualitative research can be described as ensuring that the research design, method, and conclusions are explicit, public, replicable, open to critique, and free of bias. 41 Rigor in the research process and results are achieved when each element of study methodology is systematic and transparent through complete, methodical, and accurate reporting. 33 Beginning the study with a well-developed conceptual framework and active use of both researcher reflexivity and rigorous peer review during study implementation can drive both study rigor and quality.

As the number of published qualitative studies in health professions educational research increases, it is important for our community of health care educators to keep in mind the unique aspects of rigor in qualitative studies presented here. Qualitative researchers should select and apply any of the above referenced study methods and research practices, as appropriate to the research question, to achieve rigor and quality. As in any research paradigm, the goal of quality and rigor in qualitative research is to minimize the risk of bias and maximize the accuracy and credibility of research results. Rigor is best achieved through thoughtful and deliberate planning, diligent and ongoing application of researcher reflexivity, and honest communication between the researcher and the audience regarding the study and its results.