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

机译:将GRADE-CERQual应用于定性证据综合调查结果-论文5:如何评估数据的充分性

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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.
机译:背景信息GRADE-CERQual(定性研究评论中的证据置信度)方法是由GRADE(建议书评估,开发和评估等级)工作组开发的。开发此方法的目的是支持在决策中使用定性证据综合的发现,包括指南制定和政策制定.CERQual包括四个组件,用于评估对定性研究的评论中的发现有多大的信心(也称为定性证据综合):( 1)方法学上的局限性; (2)连贯性; (3)数据是否充分; (4)相关性。本文是提供有关如何应用CERQual的指南的系列文章的一部分,并重点介绍了CERQual的数据分量是否足够。方法我们通过搜索文献中的定义,收集相关研究社区的反馈以及通过项目小组会议达成共识来开发数据成分的充分性。在就当前的定义和应用原则达成共识之前,我们通过几种定性证据综合测试了数据成分的CERQual充分性。结果在应用CERQual时,我们将数据的充分性定义为丰富程度和支持评论结果的数据量的总体确定。在本文中,我们描述了充分性组成部分及其基本原理,并提供了有关如何在CERQual方法的一部分中进行审查发现的背景下评估数据充分性的指南。本指南概述了评估数据充分性所需的信息,评估数据充分性所需采取的步骤以及充分性评估的示例。结论本文为审查作者和其他人提供了在CERQual方法的背景下进行充分性评估的指南。我们根据支持每个审查发现的数据的丰富性和数量来评估数据的充分性,但是没有提供关于什么构成足够丰富的数据或足够数量的数据的固定规则。相反,我们建议根据发现的性质进行评估。我们希望随着我们在该方法的实际实施方面的经验的增加,CERQual方法及其各个组成部分将进一步发展。

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