Ames Heather M R, France Emma F, Cooper Sara, Bianchim Mayara S, Lewin Simon, Schmidt Bey-Marrié, Uny Isabelle, Noyes Jane
Department for Systematic Reviews and Health Technology Assessments The Norwegian Institute of Public Health Oslo Norway.
Centre for Healthcare and Community Research, Faculty of Health Sciences and Sport University of Stirling Stirling UK.
Cochrane Evid Synth Methods. 2024 Jun 28;2(7):e12059. doi: 10.1002/cesm.12059. eCollection 2024 Jul.
Well-conducted qualitative evidence syntheses (QESs) can provide invaluable insights into complex phenomena. However, the development of an in-depth understanding depends on the analysis of rich, thick data from the included primary qualitative studies. Sampling may be needed if there are too many eligible studies. Data richness and thickness are among several criteria that can be taken into consideration when sampling studies for inclusion. However, existing tools do not address explicitly the assessment of both data richness and thickness in the context of QES.
To address this gap, we have developed, piloted, and conducted initial user testing of a richness and thickness assessment tool. The tool has been in development since 2014. Three pilot versions from three review teams have been used in six Cochrane reviews. Key members from the original three review teams subsequently came together to create a consensus-based definitive version 1 of the tool. Four review authors piloted the version 1 tool, which has been subject to initial user testing. The version 1 assessment tool consists of two components: assessing the thickness of contextual data and assessing the richness of conceptual data. The accompanying guidance emphasizes the importance of assessing data that addresses the review question.
The paper provides guidance on how to apply the tool, emphasizing the importance of reaching a consensus among review authors and fostering a shared understanding of what constitutes rich and thick data in the context of the review. The potential challenges related to the time and resource constraints of this additional review process are acknowledged.
Version 1 of the tool represents a significant development in QES methodology, filling a critical gap and enhancing the transparency and rigor of the sampling process. The authors invite feedback from the research community to further test, refine and improve this tool based on wider user experiences.
精心开展的定性证据综合（QES）可为复杂现象提供宝贵见解。然而，深入理解的形成依赖于对纳入的原始定性研究中的丰富、详尽数据进行分析。如果符合条件的研究过多，可能需要进行抽样。在对纳入研究进行抽样时，数据丰富性和详尽性是可考虑的若干标准之一。然而，现有工具并未明确解决在QES背景下对数据丰富性和详尽性的评估问题。
为弥补这一差距，我们开发、试用并进行了初始用户测试，以检验一种丰富性和详尽性评估工具。该工具自2014年起开始研发。三个综述团队的三个试用版本已用于六项Cochrane综述。最初三个综述团队的关键成员随后共同创建了基于共识的工具最终版本1。四位综述作者试用了版本1工具，并进行了初始用户测试。版本1评估工具由两个部分组成：评估背景数据的详尽性和评估概念数据的丰富性。随附的指南强调了评估针对综述问题的数据的重要性。
本文提供了如何应用该工具的指南，强调了综述作者之间达成共识以及促进对在综述背景下构成丰富和详尽数据的内容形成共同理解的重要性。同时也承认了这一额外综述过程在时间和资源限制方面的潜在挑战。
工具版本1代表了QES方法学的一项重大进展，填补了关键空白，提高了抽样过程的透明度和严谨性。作者邀请研究界提供反馈，以便根据更广泛的用户体验进一步测试、完善和改进该工具。
