Arash Rashidian

Using qualitative evidence in decision making for health and social interventions: an approach to assess confidence in findings from qualitative evidence syntheses (GRADE-CERQual).

Simon Lewin and colleagues present a methodology for increasing transparency and confidence in qualitative research synthesis.

Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series

The GRADE-CERQual (‘Confidence in the Evidence from Reviews of Qualitative research’) approach provides guidance for assessing how much confidence to place in findings from systematic reviews of qualitative research (or qualitative evidence syntheses). The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation. Confidence in the evidence from qualitative evidence syntheses is an assessment of the extent to which a review finding is a reasonable representation of the phenomenon of interest. CERQual provides a systematic and transparent framework for assessing confidence in individual review findings, based on consideration of four components: (1) methodological limitations, (2) coherence, (3) adequacy of data, and (4) relevance. A fifth component, dissemination (or publication) bias, may also be important and is being explored. As with the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach for effectiveness evidence, CERQual suggests summarising evidence in succinct, transparent, and informative Summary of Qualitative Findings tables. These tables are designed to communicate the review findings and the CERQual assessment of confidence in each finding. This article is the first of a seven-part series providing guidance on how to apply the CERQual approach. In this paper, we describe the rationale and conceptual basis for CERQual, the aims of the approach, how the approach was developed, and its main components. We also outline the purpose and structure of this series and discuss the growing role for qualitative evidence in decision-making. Papers 3, 4, 5, 6, and 7 in this series discuss each CERQual component, including the rationale for including the component in the approach, how the component is conceptualised, and how it should be assessed. Paper 2 discusses how to make an overall assessment of confidence in a review finding and how to create a Summary of Qualitative Findings table. The series is intended primarily for those undertaking qualitative evidence syntheses or using their findings in decision-making processes but is also relevant to guideline development agencies, primary qualitative researchers, and implementation scientists and practitioners.

Quasi-experimental study designs series-paper 4: uses and value.

Quasi-experimental studies are increasingly used to establish causal relationships in epidemiology and health systems research. Quasi-experimental studies offer important opportunities to increase and improve evidence on causal effects: (1) they can generate causal evidence when randomized controlled trials are impossible; (2) they typically generate causal evidence with a high degree of external validity; (3) they avoid the threats to internal validity that arise when participants in nonblinded experiments change their behavior in response to the experimental assignment to either intervention or control arm (such as compensatory rivalry or resentful demoralization); (4) they are often well suited to generate causal evidence on long-term health outcomes of an intervention, as well as nonhealth outcomes such as economic and social consequences; and (5) they can often generate evidence faster and at lower cost than experiments and other intervention studies.

Applying GRADE-CERQual to qualitative evidence synthesis findings—paper 2: how to make an overall CERQual assessment of confidence and create a Summary of Qualitative Findings table

BackgroundThe GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision making, including guideline development and policy formulation.CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations, (2) coherence, (3) adequacy of data and (4) relevance. This paper is part of a series providing guidance on how to apply CERQual and focuses on making an overall assessment of confidence in a review finding and creating a CERQual Evidence Profile and a CERQual Summary of Qualitative Findings table.MethodsWe developed this guidance by examining the methods used by other GRADE approaches, gathering feedback from relevant research communities and developing consensus through project group meetings. We then piloted the guidance on several qualitative evidence syntheses before agreeing on the approach.ResultsConfidence in the evidence is an assessment of the extent to which a review finding is a reasonable representation of the phenomenon of interest. Creating a summary of each review finding and deciding whether or not CERQual should be used are important steps prior to assessing confidence. Confidence should be assessed for each review finding individually, based on the judgements made for each of the four CERQual components. Four levels are used to describe the overall assessment of confidence: high, moderate, low or very low. The overall CERQual assessment for each review finding should be explained in a CERQual Evidence Profile and Summary of Qualitative Findings table.ConclusionsStructuring and summarising review findings, assessing confidence in those findings using CERQual and creating a CERQual Evidence Profile and Summary of Qualitative Findings table should be essential components of undertaking qualitative evidence syntheses. This paper describes the end point of a CERQual assessment and should be read in conjunction with the other papers in the series that provide information on assessing individual CERQual components.

Applying GRADE-CERQual to qualitative evidence synthesis findings-paper 6: how to assess relevance of the data

BackgroundThe GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation.CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations, (2) coherence, (3) adequacy of data and (4) relevance. This paper is part of a series providing guidance on how to apply CERQual and focuses on CERQual’s relevance component.MethodsWe developed the relevance component by searching the literature for definitions, gathering feedback from relevant research communities and developing consensus through project group meetings. We tested the CERQual relevance component within several qualitative evidence syntheses before agreeing on the current definition and principles for application.ResultsWhen applying CERQual, we define relevance as the extent to which the body of data from the primary studies supporting a review finding is applicable to the context (perspective or population, phenomenon of interest, setting) specified in the review question. In this paper, we describe the relevance component and its rationale and offer guidance on how to assess relevance in the context of a review finding. This guidance outlines the information required to assess relevance, the steps that need to be taken to assess relevance and examples of relevance assessments.ConclusionsThis paper provides guidance for review authors and others on undertaking an assessment of relevance in the context of the CERQual approach. Assessing the relevance component requires consideration of potentially important contextual factors at an early stage in the review process. We expect the CERQual approach, and its individual components, to develop further as our experiences with the practical implementation of the approach increase.

Applying GRADE-CERQual to qualitative evidence synthesis findings-paper 4: how to assess coherence

BackgroundThe GRADE-CERQual (Grading of Recommendations Assessment, Development and Evaluation-Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE working group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation.CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations, (2) relevance, (3) coherence and (4) adequacy of data. This paper is part of a series providing guidance on how to apply CERQual and focuses on CERQual’s coherence component.MethodsWe developed the coherence component by searching the literature for definitions, gathering feedback from relevant research communities and developing consensus through project group meetings. We tested the CERQual coherence component within several qualitative evidence syntheses before agreeing on the current definition and principles for application.ResultsWhen applying CERQual, we define coherence as how clear and cogent the fit is between the data from the primary studies and a review finding that synthesises that data. In this paper, we describe the coherence component and its rationale and offer guidance on how to assess coherence in the context of a review finding as part of the CERQual approach. This guidance outlines the information required to assess coherence, the steps that need to be taken to assess coherence and examples of coherence assessments.ConclusionsThis paper provides guidance for review authors and others on undertaking an assessment of coherence in the context of the CERQual approach. We suggest that threats to coherence may arise when the data supporting a review finding are contradictory, ambiguous or incomplete or where competing theories exist that could be used to synthesise the data. We expect the CERQual approach, and its individual components, to develop further as our experiences with the practical implementation of the approach increase.

Applying GRADE-CERQual to qualitative evidence synthesis findings — paper 5: how to assess adequacy of data.

BackgroundThe GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) working group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation.CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations; (2) coherence; (3) adequacy of data; and (4) relevance. This paper is part of a series providing guidance on how to apply CERQual and focuses on CERQual’s adequacy of data component.MethodsWe developed the adequacy of data component by searching the literature for definitions, gathering feedback from relevant research communities and developing consensus through project group meetings. We tested the CERQual adequacy of data component within several qualitative evidence syntheses before agreeing on the current definition and principles for application.ResultsWhen applying CERQual, we define adequacy of data as an overall determination of the degree of richness and the quantity of data supporting a review finding. In this paper, we describe the adequacy component and its rationale and offer guidance on how to assess data adequacy in the context of a review finding as part of the CERQual approach. This guidance outlines the information required to assess data adequacy, the steps that need to be taken to assess data adequacy, and examples of adequacy assessments.ConclusionsThis paper provides guidance for review authors and others on undertaking an assessment of adequacy in the context of the CERQual approach. We approach assessments of data adequacy in terms of the richness and quantity of the data supporting each review finding, but do not offer fixed rules regarding what constitutes sufficiently rich data or an adequate quantity of data. Instead, we recommend that this assessment is made in relation to the nature of the finding. We expect the CERQual approach, and its individual components, to develop further as our experiences with the practical implementation of the approach increase.

Applying GRADE-CERQual to qualitative evidence synthesis findings—paper 3: how to assess methodological limitations

BackgroundThe GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation.CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations, (2) coherence, (3) adequacy of data and (4) relevance. This paper is part of a series providing guidance on how to apply CERQual and focuses on CERQual’s methodological limitations component.MethodsWe developed the methodological limitations component by searching the literature for definitions, gathering feedback from relevant research communities and developing consensus through project group meetings. We tested the CERQual methodological limitations component within several qualitative evidence syntheses before agreeing on the current definition and principles for application.ResultsWhen applying CERQual, we define methodological limitations as the extent to which there are concerns about the design or conduct of the primary studies that contributed evidence to an individual review finding. In this paper, we describe the methodological limitations component and its rationale and offer guidance on how to assess methodological limitations of a review finding as part of the CERQual approach. This guidance outlines the information required to assess methodological limitations component, the steps that need to be taken to assess methodological limitations of data contributing to a review finding and examples of methodological limitation assessments.ConclusionsThis paper provides guidance for review authors and others on undertaking an assessment of methodological limitations in the context of the CERQual approach. More work is needed to determine which criteria critical appraisal tools should include when assessing methodological limitations. We currently recommend that whichever tool is used, review authors provide a transparent description of their assessments of methodological limitations in a review finding. We expect the CERQual approach and its individual components to develop further as our experiences with the practical implementation of the approach increase.

Applying GRADE-CERQual to qualitative evidence synthesis findings–paper 7: understanding the potential impacts of dissemination bias

BackgroundThe GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation.CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations, (2) coherence, (3) adequacy of data and (4) relevance. This paper is part of a series providing guidance on how to apply CERQual and focuses on a probable fifth component, dissemination bias. Given its exploratory nature, we are not yet able to provide guidance on applying this potential component of the CERQual approach. Instead, we focus on how dissemination bias might be conceptualised in the context of qualitative research and the potential impact dissemination bias might have on an overall assessment of confidence in a review finding. We also set out a proposed research agenda in this area.MethodsWe developed this paper by gathering feedback from relevant research communities, searching MEDLINE and Web of Science to identify and characterise the existing literature discussing or assessing dissemination bias in qualitative research and its wider implications, developing consensus through project group meetings, and conducting an online survey of the extent, awareness and perceptions of dissemination bias in qualitative research.ResultsWe have defined dissemination bias in qualitative research as a systematic distortion of the phenomenon of interest due to selective dissemination of studies or individual study findings. Dissemination bias is important for qualitative evidence syntheses as the selective dissemination of qualitative studies and/or study findings may distort our understanding of the phenomena that these syntheses aim to explore and thereby undermine our confidence in these findings.Dissemination bias has been extensively examined in the context of randomised controlled trials and systematic reviews of such studies. The effects of potential dissemination bias are formally considered, as publication bias, within the GRADE approach. However, the issue has received almost no attention in the context of qualitative research. Because of very limited understanding of dissemination bias and its potential impact on review findings in the context of qualitative evidence syntheses, this component is currently not included in the GRADE-CERQual approach.ConclusionsFurther research is needed to establish the extent and impacts of dissemination bias in qualitative research and the extent to which dissemination bias needs to be taken into account when we assess how much confidence we have in findings from qualitative evidence syntheses.

Target income and its determinants for general physicians: An instrumental variables approach

ABSTRACT The economic behavior of physicians has a considerable effect on healthcare expenditure. A well-known theory for explaining physicians’ behavior is the target income hypothesis. In this theory, the main issue is concerned with how physicians set their desired income. Therefore, in this study the level and determinants of Iranian general practitioners’ (GPs’) desired income were analyzed. Respondents consisted of 666 GPs. We used instrumental variables (IV) as predictors of actual income in order to address endogeneity and possible measurement error in it. The monthly mean (standard deviation) of target income and the actual income of Iranian GPs were 4877.4 (3445.7) and 2188 (1768.9) USD. The actual income was the most important determinant of target income. GPs with higher economic expectations, as well as GPs dissatisfied with their current professional financial status, and GPs that had a greater willingness to save money set higher target incomes in order to respond to these expectations, to compensate their financial dissatisfaction, and to address the economic instability and financial risk of their practice. Focusing on financial incentives only in the short term can change their behavior, however in the long term, policies related to controlling desired income with focus on views factors can be helpful.