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No Mechanism Without Context: Strengthening the Analysis of Context in Realist Evaluations Using Causal Loop Diagramming
Author(s) -
Renmans Dimitri,
Holvoet Nathalie,
Criel Bart
Publication year - 2020
Publication title -
new directions for evaluation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.374
H-Index - 40
eISSN - 1534-875X
pISSN - 1097-6736
DOI - 10.1002/ev.20424
Subject(s) - causal loop diagram , context (archaeology) , intervention (counseling) , agency (philosophy) , mechanism (biology) , set (abstract data type) , knowledge management , process management , computer science , risk analysis (engineering) , public relations , management science , psychology , business , political science , sociology , economics , epistemology , paleontology , social science , philosophy , artificial intelligence , system dynamics , psychiatry , biology , programming language
Realist evaluation is an approach with a strong emphasis on causal mechanisms and the context in which they are triggered. However, recent reviews of published realist evaluations show that context is often understudied. This is problematic, as a thorough understanding of the relationship between context and causal mechanisms is crucial in assisting policymakers to make appropriate and targeted decisions that improve the intervention. Therefore, we set out to test whether combining realist evaluation with the “systems thinking” approach and, more specifically, causal loop diagramming, could help strengthen the analysis of context. We did this through a study of a performance‐based financing (PBF) intervention in the Ugandan health care sector by the Belgian development agency, Enabel. PBF allocates funds to health workers and/or health facilities based on their performance, and introduces additional management support tools, provides extra monitoring and supervision, and promotes community participation in management issues, among other activities. In this case, we found that the proposed combined methodological approach indeed adds value to the analysis, as it leads to insights into the role played by the underlying system that otherwise may have been overlooked. Moreover, such information may provide clear directions to policymakers on how to improve the intervention in a sustainable way. Finally, causal loop diagrams help to visualize complex causal interactions and to communicate them to policymakers.