What’s in a Realist Configuration? Deciding Which Causal Configurations to Use, How, and Why

摘要

Background: Realist studies represent an increasingly popular approach for exploring complex interventions’ successes and failures. The theory-driven approach seeks to explain “what works, how, why, in which contexts, for whom, and to what extent” using context–mechanism–outcome (CMO) configurations. When the approach was first developed, CMO configurations were the method for expressing causal explanations. Increasingly, realist studies have been conducted using different variations of the heuristic such as strategy–context–mechanism–outcome (SCMO) configurations or intervention–context–actor–mechanism– outcome (ICAMO) configurations. Researchers have highlighted a lack of methodological guidance regarding which additional explanatory factors can be included in configurations (e.g., strategies, interventions, actors). This article aims to clarify and further develop the concept of configurations by discussing how explanatory factors could be robustly added to the original CMO configuration as put forward by Pawson and Tilley. Comparing the use of different types of configurations: We draw on two of our own studies, one which formulated CMO configurations and one which formulated SCMO configurations, and on an evidence scan of realist studies. We explored the effects these different configurations had on studies’ findings and highlight why researchers chose CMOs or SCMOs. Finally, we provide recommendations regarding the use of configurations. These are as follows: Using additional explanatory factors is possible but consider the research scope to select the configuration appropriate for the study; Be transparent about the choice in configuration and include examples of configurations; Further studies about the use of additional explanatory factors are needed to better understand the effects on each step in the realist evaluation cycle; and New ways of disseminating realist findings are needed to balance transparency regarding the use of configurations. Conclusions: Adding explanatory factors is possible and can be insightful depending on the study’s scope and aims; however, any configuration type must adhere to the rule of generative causation.