Purposively sampling for qualitative evidence syntheses: Methodological lessons from a synthesis on vaccination communication

Article type
Authors
Ames H1, Lewin S2, Glenton C1
1The Norwegian Institute of Public Health
2The Norwegian Institute of Public Health and South African Medical Research Council
Abstract
Background: In a qualitative evidence synthesis (QES), too much data due to a very large number of included studies can undermine thorough analysis. In order to limit the number of included studies included in a QES on vaccination communication, we developed and applied a sampling framework to studies that met our inclusion criteria.

Objectives: To discuss the development and application/strengths and weaknesses of a sampling framework for a QES on vaccination communication.

Methods: We mapped eligible studies by extracting key information from each study, for example: country, study setting, vaccine, data richness, and study objectives. The final sampling frame included the following three steps:
1. Include studies set in low- and middle-income (LMIC) settings
2. Include studies scoring a three or more on a scale of data richness developed for this synthesis
3. Include studies where the study objectives closely match the synthesis objectives

Results: Seventy studies were eligible for inclusion in the review. Thirty-eight studies were sampled for inclusion in the synthesis. Nine studies were sampled in round one from LMIC contexts. These studies contributed to, on average, the least number of findings in the final synthesis. Twenty-four studies were sampled in round two on the basis of data richness. These studies mostly contributed to a larger number of findings. The five studies sampled in round three that, from the studies remaining at that stage, most closely matched the synthesis objectives contributed on average to a large number of findings.

Conclusions: Our approach to purposive sampling allowed us to achieve a wider geographic spread of articles and to increase the number of included studies that had rich data and closely matched the synthesis objective. It is possible that we may have overlooked articles 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. Ways of cross-checking for under-represented themes are needed.