Developing and testing intervention theory by incorporating a views synthesis into a qualitative comparative analysis of intervention effectiveness

Abstract

Qualitative comparative analysis (QCA) was originally developed as a tool for cross‐national comparisons in macrosociology, but its use in evaluation and evidence synthesis of complex interventions is rapidly developing. QCA is theory‐driven and relies on Boolean logic to identify pathways to an outcome (eg, is the intervention effective or not?). We use the example of two linked systematic reviews on weight management programs (WMPs) for adults—one focusing on user views (a “views synthesis”) and one focusing on the effectiveness of WMPs incorporating dietary and physical activity—to demonstrate how a synthesis of user views can supply a working theory to structure a QCA. We discuss how a views synthesis is especially apt to supply this working theory because user views can (a) represent a “middle‐range theory” of the intervention; (b) bring a participatory, democratic perspective; and (c) provide an idiographic understanding of how the intervention works that external taxonomies may not be able to furnish. We then discuss the practical role that the views synthesis played in our QCA examining pathways to effectiveness: (a) by suggesting specific intervention features and sharpening the focus on the most salient features to be examined, (b) by supporting interpretation of findings, and (c) by bounding data analysis to prevent data dredging.

How do moral hazard behaviors lead to the waste of medical insurance funds? An empirical study from China

“…It is fit to deal with the results of multiple conditions and multiple factor combinations in complex social situations, and it can also analyze the conditional combinations that lead to the asymmetric causality of social phenomena ( 31 , 32 ). In recent years, QCA begun to explore the field of medical and health care research gradually ( 29 , 33 – 35 ).…”

Structural Interventions to Enable Adolescent Contraceptive Use in LMICs: A Mid-Range Theory to Support Intervention Development and Evaluation

“…We then focused on the ‘likely effective’ and ‘likely ineffective’ studies to explore what could explain the difference between these two sets. This is a method that has previously been used in qualitative comparative analysis (QCA) and allows a focus on the differences between these two groups, avoiding the ‘noise’ from those achieving a moderate effect [ 14 ]. This is important since heterogeneity is of critical importance in QCA in order to identify which combinations of characteristics are sufficient to explain the outcomes [ 15 ].…”

Interventions targeting healthcare providers to optimise use of caesarean section: a qualitative comparative analysis to identify important intervention features

“…Firstly, we derived potential conditions deductively using our logic model (Additional File 1 ). Secondly, additional potential conditions were inductively derived from each eligible case using qualitative evidence “views” synthesis using Melendez-Torres’s approach [ 69 ] and intervention component analysis [ 71 ], where we examine potential conditions based on trialist’ reflections. The trialist’ reflections typically can be found in the discussion section of the paper and included contextual conditions like healthcare providers’ beliefs on CS, providers’ willingness to change, institutional culture, baseline CS rates, and policy relating CS.…”

“…Adhering to the “views synthesis as theory” perspective [ 69 ] , existing qualitative evidence syntheses and theoretical literature were used to guide the construction of truth tables. Our truth tables examined potential configurations of sufficient and necessary intervention, implementation and contextual conditions associated with a reduction in CS rates.…”

“…For studies that were not assessed by either Chen et al [19] or Opiyo et al [30], we assessed risk of bias using the same tools as used in the original review depending on their study design (Additional File 2 risk of bias assessment). We excluded studies assessed as high risk of bias, which may resulted us in missing information from relevant studies, yet necessary to ensure that this analysis is based on high quality studies and to allow researchers to develop deep case knowledge by limiting the overall number of included studies [69,70].…”