Realist analysis of qualitative data in health professions education research.

Background: Qualitative realist analysis is gaining in popularity in health professions education research (HPER) as part of theory-driven program evaluation. Although realist approaches such as syntheses and evaluations typically advocate mixed methods, qualitative data dominate currently. Various forms of qualitative analysis have been articulated in HPER, yet realist analysis has not. Although realist analysis is interpretive, it moves beyond description to explain generative causation employing retroductive theorising. Ultimately, it attempts to build and/or 'test' (confirm, refute or refine) theory about how, why, for whom, when and to what extent programs work using the context-mechanism-outcome configuration (CMOC) heuristic. This paper aims to help readers better critique, conduct and report qualitative realist analysis.

Realist analysis methods: We describe four fundamentals of qualitative realist analysis: (1) simultaneous data collection/analysis; (2) retroductive theorising; (3) configurational analysis (involving iterative phases of identifying CMOCs, synthesising CMOCs into demi-regularities and translating demi-regularities into program theory); and (4) realist analysis quality (relevance, rigour, richness). Next, we provide a critical analysis of realist analyses employed in 15 HPER outputs-three evaluations and 12 syntheses. Finally, drawing on our understandings of realist literature and our experiences of conducting qualitative realist analysis (both evaluations and syntheses), we articulate three common analysis challenges (coding, consolidation and mapping) and strategies to mitigate these challenges (teamwork, reflexivity and consultation, use of data analysis software and graphical representations of program theory).

Conclusions: Based on our critical analysis of the literature and realist analysis experiences, we encourage researchers, peer reviewers and readers to better understand qualitative realist analysis fundamentals. Realist analysts should draw on relevant realist reporting standards and literature on realist analysis to improve the quality and reporting of realist analysis. Through better understanding the common challenges and mitigation strategies for realist analysis, we can collectively improve the quality of realist analysis in HPER.