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Journal Article

Citation

Frerichs L, Lich KH, Funchess M, Burrell M, Cerulli C, Bedell P, White AM. Prog. Community Health Partnersh. 2016; 10(3): 443-459.

Copyright

(Copyright © 2016, Johns Hopkins University Press)

DOI

10.1353/cpr.2016.0051

PMID

28230552

Abstract

BACKGROUND: Group model building (GMB) is an approach to building qualitative and quantitative models with stakeholders to learn about the interrelationships among multilevel factors causing complex public health problems over time. Scant literature exists on adapting this method to address public health issues that involve racial dynamics.

OBJECTIVES: This study's objectives are to (1) introduce GMB methods, (2) present a framework for adapting GMB to enhance cultural responsiveness, and (3) describe outcomes of adapting GMB to incorporate differences in racial socialization during a community project seeking to understand key determinants of community violence transmission.

METHODS: An academic-community partnership planned a 1-day session with diverse stakeholders to explore the issue of violence using GMB. We documented key questions inspired by critical race theory (CRT) and adaptations to established GMB "scripts" (i.e., published facilitation instructions). The theory's emphasis on experiential knowledge led to a narrative-based facilitation guide from which participants created causal loop diagrams. These early diagrams depict how violence is transmitted and how communities respond, based on participants' lived experiences and mental models of causation that grew to include factors associated with race.

CONCLUSIONS: Participants found these methods useful for advancing difficult discussion. The resulting diagrams can be tested and expanded in future research, and will form the foundation for collaborative identification of solutions to build community resilience. GMB is a promising strategy that community partnerships should consider when addressing complex health issues; our experience adapting methods based on CRT is promising in its acceptability and early system insights.


Language: en

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