Abstract for: A qualitative system dynamics model of overdose bystander behavior in the context of Connecticut’s Good Samaritan Laws
Good Samaritan Laws (GSLs) are a harm reduction policy intended to reduce fatal opioid overdose by enabling bystanders, first responders, and health care providers to assist individuals experiencing an overdose without facing civil or criminal liability. GSLs may not be reaching their full impact in many communities due to lack of knowledge of GSL protections among other poorly understood implementation barriers. The purpose of this study was to develop a systems understanding of the factors influencing bystander responses to opioid overdose in the context of Connecticut’s GSLs and to identify high-leverage policies for improving GSL implementation in Connecticut. We conducted six group model building workshops that engaged a diverse set of participants with medical and community expertise and lived bystander experience. Through an iterative, stakeholder-engaged process, we developed and refined a qualitative system dynamics model in the form of a causal loop diagram. Our model, grounded in local knowledge and experience, brings a nuanced systems perspective to the literature on bystander behavior in the context of GSLs, showing how non-linear interdependencies of the social, structural, and policy determinants of bystander behavior collectively form endogenous feedbacks which can be leveraged to design policies to advance systems change.