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This study was supported by the Agency for Healthcare Research and Quality, Grant Award Number 1R18HS027080-01. The content provided is solely the responsibility of the authors and does not necessarily represent the official views of the funders.
Published In
Systems Research and Behavioral Science
Document Type
Article
Publication Date
6-14-2024
Subjects
System theory -- modeling
Abstract
Qualitative data are commonly used in the development of system dynamicsmodels, but methods for systematically identifying causal structures in qualita-tive data have not been widely established. This article presents a modifiedprocess for identifying causal structures (e.g., feedback loops) that are commu-nicated implicitly or explicitly and utilizes software to make coding, tracking,and model rendering more efficient. This approach draws from existingmethods, system dynamics best practice, and qualitative data analysis tech-niques. Steps of this method are presented along with a description of causalstructures for an audience new to system dynamics. The method is applied to aset of interviews describing mental models of clinical practice transformationfrom an implementation study of screening and treatment for unhealthy alco-hol use in primary care. This approach has the potential to increase rigour andtransparency in the use of qualitative data for model building and to broadenthe user base for causal-loop diagramming.
Rights
Copyright (c) 2024 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1002/sres.3030
Persistent Identifier
https://archives.pdx.edu/ds/psu/42202
Citation Details
Kenzie, E. S., Wakeland, W., Jetter, A., Lich, K. H., Seater, M., & Davis, M. M. (2024). Mapping mental models through an improved method for identifying causal structures in qualitative data. Systems Research and Behavioral Science. Portico.