Community‐based system dynamics modelling of stigmatized public health issues: Increasing diverse representation of individuals with personal experiences

Abstract

Utility of community-based system dynamics (CBSD) models on stigmatized public health issues and health disparities depends upon how representative the model is to real-world experience within the community. 'Personal experience' participants (PEP), especially from marginalized groups, are essential model contributors but are often underrepresented in modelling groups due to multiple barriers. This study details a method to increase PEP representation for models on stigmatized issues. We use a case study from a CBSD project on health disparities within the association between alcohol misuse (AM) and intimate partner violence (IPV) for Northern Plains Indigenous women. Short group model building sessions were held at three community organizations providing relevant resources. Each model contributed unique system components, and there were few similarities between models. A consolidated model provided a rich picture of the complex system. Adding brief PEP-based group modelling sessions can enhance PEP representation in model development for stigmatized public health issues.

Applications of Participatory System Dynamics Methods to Public Health

“…29 One of the more powerful advantages of applying a systems thinking lens to public health problems is the ability of SD and system diagrams to demonstrate and communicate complexity effectively. [30][31][32][33][34][35] By utilizing stakeholder input to develop system diagrams, PSD methods allow for the explicit acknowledgment of the complexity of wicked public health problems. [30][31][32][33][34][35] Several articles described PSD methods as approachable tools for understanding and communicating complex concepts into understandable stories accessible to a wide variety of professionals and laypersons alike.…”

“…[30][31][32][33][34][35] By utilizing stakeholder input to develop system diagrams, PSD methods allow for the explicit acknowledgment of the complexity of wicked public health problems. [30][31][32][33][34][35] Several articles described PSD methods as approachable tools for understanding and communicating complex concepts into understandable stories accessible to a wide variety of professionals and laypersons alike. 30,31 The collective act of creating CLDs in PSD methods facilitates a visual storytelling practice that not only accessibly communicates complex concepts H. Decker and M. Wendel…”

“…but also facilitates equitable and social learning processes among stakeholders. 25,27,28,30,34,[36][37][38][39][40][41] Causal loop diagrams are compelling because they offer the unique capability of conveying complex information effectively and parsimoniously and may also be more engaging and interesting than traditional graphs and charts. 37 The act of engaging in visual storytelling by building CLDs helps stakeholders understand the "potential for nonlinear relationships between contributing factors and unanticipated emergent behavior of complex systems," 38(p2) which allows for increased ability to identify leverage points for intervention that may not otherwise be obvious.…”

“…Reconceptualizing public health issues as complex, dynamic, and adaptive systems broadens and shifts existing perspectives of the central issue, which allows for the identification of innovative leverage points of intervention. 23,25,30,32,33,[42][43][44][45][46][47][48][49][50][51] Another benefit of visualizing public health issues as complex and adaptive systems is that it allows participants to think creatively about solutions, and even identify meaningful solutions that otherwise may have seemed counterintuitive using conventional research approaches. 33,43 Engaging in PSD is thus helpful in developing creative approaches to intervention that avoid "band-aid" solutions.…”

Grounding alcohol simulation models in empirical and theoretical alcohol research: a model for a Northern Plains population in the United States

“…Detail on data/information used for model structure rationale varied from thorough discussions of prior literature (Clapp et al ., 2018; Mubayi et al ., 2010) to minimal references (Tawileh et al ., 2008). Studies that utilized participatory model building often discussed professional and personal expertise; however, few studies mentioned participants' specific academic, professional, or personal expertise/experience with AU (Belue et al ., 2012; Deutsch et al ., 2021; Matson et al ., 2021). Studies that used alcohol research for structure rationale focused on AU influences (e.g.…”