How Are Qualitative Methods Used in Implementation Science Research? Results From a Systematic Scoping Review

Qualitative methods are essential for providing an in-depth understanding of "why" and "how" evidence-based interventions are successfully implemented-a key area of implementation science (IS) research. A systematic synthesis of the applications of qualitative methods is critical for understanding how qualitative methods have been used to date and identifying areas of innovation and optimization. This scoping review explores which qualitative data collection and analytic methods are used in IS research, what and how frameworks and theories are leveraged using qualitative methods, and which implementation issues are explored with qualitative implementation research.

We conducted a systematic scoping review of articles in MEDLINE and Embase using qualitative methods in IS health research. We systematically extracted information including study design, data collection method(s), analytic method(s), implementation outcomes, and other domains.

Our search yielded a final dataset of 867 articles from 76 countries. Qualitative study designs were predominantly single elicitation (67.7%) and longitudinal (20.3%). In-depth interviews were the most common data collection method (84.3%), followed by focus group discussions (FGDs) (34.5%), and nearly 25% used both. Sample sizes were, on average, 40 in-depth interviews (range: 1-1,131) and nine FGDs (range: 1-46). The most common analytic approaches were thematic analysis (45.3%) and content analysis (18.5%) with substantial variation in analytic conceptualization. Nearly one-quarter (23.2%) of articles used one or more TMF to conceptualize the study, and less than half (40.9%) of articles used a TMF to guide both data collection and analysis.

We highlight variation in how qualitative methods were used, as well as detailed examples of data collection and analysis descriptions. By reviewing how qualitative methods have been used in well-described and innovative ways, and identifying important gaps, we highlight opportunities for strengthening their use to optimize IS research.

The protocol can be found 10.11124/JBIES-20-00120.