评估定性数据的丰富性和厚度:开发用于定性证据综合的循证工具

Heather M. R. Ames, Emma F. France, Sara Cooper, Mayara S. Bianchim, Simon Lewin, Bey-Marrié Schmidt, Isabelle Uny, Jane Noyes
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引用次数: 0

摘要

背景进行良好的定性证据综述(QES)可以为复杂的现象提供宝贵的见解。然而,深入理解的形成有赖于对所纳入的主要定性研究中丰富、厚实的数据进行分析。如果符合条件的研究太多,可能需要进行抽样。在对研究进行取样时,数据的丰富程度和厚度是可以考虑的几个标准之一。然而,现有的工具并没有明确解决 QES 中数据丰富度和厚度的评估问题。 方法 为了弥补这一不足,我们开发、试用了丰富度和厚度评估工具,并进行了初步用户测试。该工具自 2014 年开始开发。来自三个评审团队的三个试点版本已在六个 Cochrane 评审中使用。最初三个综述团队的主要成员随后聚集在一起,在协商一致的基础上创建了该工具的最终第 1 版。四位综述作者试用了第一版工具,并对其进行了初步的用户测试。第 1 版评估工具由两部分组成:评估背景数据的厚度和评估概念数据的丰富程度。随附的指南强调了评估解决审查问题的数据的重要性。 结果 本文就如何应用该工具提供了指导,强调了在综述作者之间达成共识的重要性,以及促进对综述背景下丰富和厚实数据的共同理解。同时也承认,由于时间和资源的限制,在这一额外的评审过程中可能会遇到一些挑战。 结论 该工具的第 1 版代表了 QES 方法学的重大发展,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意见,以便根据更广泛的用户经验进一步测试、完善和改进该工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Assessing qualitative data richness and thickness: Development of an evidence-based tool for use in qualitative evidence synthesis

Background

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.

Methods

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.

Results

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.

Conclusion

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.

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