Cochrane Consumers and Communication Group, La Trobe University, Melbourne, Australia.
Division for Health Services, Norwegian Institute of Public Health, Postboks 222 Skøyen, Sandakerveien 24C, inngang D11, 0213, Oslo, Norway.
BMC Med Res Methodol. 2019 Jan 31;19(1):26. doi: 10.1186/s12874-019-0665-4.
In a qualitative evidence synthesis, too much data due to a large number of studies can undermine our ability to perform a thorough analysis. Purposive sampling of primary studies for inclusion in the synthesis is one way of achieving a manageable amount of data. The objective of this article is to describe the development and application of a sampling framework for a qualitative evidence synthesis on vaccination communication.
We developed and applied a three-step framework to sample studies from among those eligible for inclusion in our synthesis. We aimed to prioritise studies that were from a range of settings, were as relevant as possible to the review, and had rich data. We extracted information from each study about country and study setting, vaccine, data richness, and study objectives and applied the following sampling framework: 1. Studies conducted in low and middle income settings 2. Studies scoring four or more on a 5-point scale of data richness 3. Studies where the study objectives closely matched our synthesis objectives RESULTS: We assessed 79 studies as eligible for inclusion in the synthesis and sampled 38 of these. First, we sampled all nine studies that were from low and middle-income countries. These studies contributed to the least number of findings. We then sampled an additional 24 studies that scored high for data richness. These studies contributed to a larger number of findings. Finally, we sampled an additional five studies that most closely matched our synthesis objectives. These contributed to a large number of findings.
Our approach to purposive sampling helped ensure that we included studies representing a wide geographic spread, rich data and a focus that closely resembled our synthesis objective. It is possible that we may have overlooked primary studies that did not meet our sampling criteria but would have contributed to the synthesis. For example, two studies on migration and access to health services did not meet the sampling criteria but might have contributed to strengthening at least one finding. We need methods to cross-check for under-represented themes.
在定性证据综合中，由于研究数量众多，可能会导致数据过多，从而削弱我们进行全面分析的能力。有针对性地选择纳入综合研究的初级研究是实现可管理数据量的一种方法。本文的目的是描述一种用于疫苗接种沟通定性证据综合的抽样框架的开发和应用。
我们开发并应用了一个三步框架，从有资格纳入综合研究的研究中进行抽样。我们旨在优先选择来自不同环境、与综述尽可能相关且数据丰富的研究。我们从每项研究中提取有关国家和研究环境、疫苗、数据丰富度以及研究目标的信息，并应用以下抽样框架：1. 在中低收入国家开展的研究；2. 数据丰富度评分达到 5 分制的 4 分或以上的研究；3. 研究目标与综合目标密切匹配的研究。
我们评估了 79 项符合纳入综合研究标准的研究，并从中抽取了 38 项。首先，我们抽取了所有 9 项来自中低收入国家的研究。这些研究贡献了最少的研究结果。然后，我们又抽取了 24 项数据丰富度评分较高的研究。这些研究贡献了更多的研究结果。最后，我们抽取了另外 5 项与我们的综合目标最匹配的研究。这些研究贡献了大量的研究结果。
我们有针对性的抽样方法有助于确保纳入研究代表广泛的地理分布、丰富的数据和与我们综合目标密切相似的重点。我们可能忽略了不符合抽样标准但可能有助于综合研究的主要研究。例如，两项关于移民和获得卫生服务的研究不符合抽样标准，但可能有助于加强至少一项研究结果。我们需要方法来交叉检查代表性不足的主题。
