Applying qualitative comparative analysis in a systematic review: Lessons learned

IF 1.7 Q4 HEALTH POLICY & SERVICES
Dorothee Bauernschmidt , Janina Wittmann , Julian Hirt , Gabriele Meyer , Anja Bieber
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引用次数: 0

Abstract

Background

Evidence synthesis of primary studies assessing complex interventions poses challenges due to the heterogeneity of study populations, interventions, outcomes, or study designs. Qualitative comparative analysis (QCA) aims to identify conditions or combinations of conditions that lead to a specific outcome and may be an appropriate instrument to deal with heterogeneity and complexity.

Objective

We aimed to describe the lessons learned when applying QCA in a systematic review on technology-based counselling interventions in dementia.

Methods

The lessons learned were generated through research team reflection and discussion of the challenges and problems encountered in the process of applying the initial steps of the QCA. As the QCA remained incomplete, a brief account of aspects to be considered when using QCA methodology for data synthesis within a systematic review is presented.

Results

The lessons learned comprise the importance of clear eligibility criteria representing the core elements of interventions and the need for a consistent dataset based on sufficient reporting and suitable publication types. We also recommend adoption of a multi-perspective view by integrating theoretical and practical knowledge.

Conclusion

QCA may increase knowledge gain in systematic reviews by capturing the complexity of interventions and contexts. An adequate dataset is needed to enable systematic comparison. To achieve this, adherence to frameworks guiding the development, implementation, and evaluation of complex interventions as well as to reporting guidelines is essential.
在系统回顾中应用定性比较分析:经验教训。
背景:由于研究人群、干预措施、结果或研究设计的异质性,评估复杂干预措施的初步研究的证据合成面临挑战。定性比较分析(QCA)旨在识别导致特定结果的条件或条件组合,可能是处理异质性和复杂性的适当工具。目的:我们的目的是描述在对基于技术的痴呆咨询干预进行系统回顾时应用QCA的经验教训。方法:通过研究团队对QCA初始步骤实施过程中遇到的挑战和问题进行反思和讨论,得出经验教训。由于QCA仍然不完整,简要介绍了在系统评价中使用QCA方法进行数据合成时要考虑的方面。结果:吸取的经验教训包括明确资格标准的重要性,代表干预措施的核心要素,以及基于充分报告和适当出版类型的一致数据集的必要性。我们还建议采用多视角的观点,将理论和实践知识结合起来。结论:QCA可以通过捕获干预措施和背景的复杂性来增加系统评价中的知识获取。需要一个足够的数据集来进行系统的比较。为实现这一目标,遵守指导复杂干预措施的制定、实施和评估的框架以及报告准则至关重要。
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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来源期刊
CiteScore
1.90
自引率
18.20%
发文量
129
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