• Monika Kastner, Julie Makarski, Leigh Hayden, Lisa Durocher, Ananda Chatterjee, Melissa Brouwers, and Onil Bhattacharyya.
• St, Michael's Hospital Li Ka Shing Knowledge Institute, 209 Victoria Street, Toronto M5B 1W8, ON, Canada. monika.kastner@utoronto.ca.
• Bmc Med Res Methodol. 2013 Sep 12; 13: 112.

BackgroundRealist reviews offer a rigorous method to analyze heterogeneous data emerging from multiple disciplines as a means to develop new concepts, understand the relationships between them, and identify the evidentiary base underpinning them. However, emerging synthesis methods such as the Realist Review are not well operationalized and may be difficult for the novice researcher to grasp. The objective of this paper is to describe the development of an analytic process to organize and synthesize data from a realist review.MethodsClinical practice guidelines have had an inconsistent and modest impact on clinical practice, which may in part be due to limitations in their design. This study illustrates the development of a transparent method for organizing and analyzing a complex data set informed by a Realist Review on guideline implementability to better understand the characteristics of guidelines that affect their uptake in practice (e.g., clarity, format). The data organization method consisted of 4 levels of refinement: 1) extraction and 2) organization of data; 3) creation of a conceptual map of guideline implementability; and 4) the development of a codebook of definitions.ResultsThis new method is comprised of four steps: data extraction, data organization, development of a conceptual map, and operationalization vis-a-vis a codebook. Applying this method, we extracted 1736 guideline attributes from 278 articles into a consensus-based set of categories, and collapsed them into 5 core conceptual domains for our guideline implementability map: Language, Format, Rigor of development, Feasibility, Decision-making.ConclusionsThis study advances analysis methods by offering a systematic approach to analyzing complex data sets where the goals are to condense, organize and identify relationships.

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