Enhancing Dissemination and Implementation Research Using Systems Science Methods

Authored by Jennifer Watling Neal, Jessica G Burke, Kristen Hassmiller Lich, Helen I Meissner, Michael Yonas, Patricia L Mabry

Date Published: 2015

DOI: 10.1007/s12529-014-9417-3

Sponsors: No sponsors listed

Platforms: NetLogo

Model Documentation: AORML Other Narrative

Model Code URLs: Model code not found

Abstract

Dissemination and implementation (D\&I) research seeks to understand and overcome barriers to adoption of behavioral interventions that address complex problems, specifically interventions that arise from multiple interacting influences crossing socio-ecological levels. It is often difficult for research to accurately represent and address the complexities of the real world, and traditional methodological approaches are generally inadequate for this task. Systems science methods, expressly designed to study complex systems, can be effectively employed for an improved understanding about dissemination and implementation of evidence-based interventions. The aims of this study were to understand the complex factors influencing successful D\&I of programs in community settings and to identify D\&I challenges imposed by system complexity. Case examples of three systems science methods-system dynamics modeling, agent-based modeling, and network analysis-are used to illustrate how each method can be used to address D\&I challenges. The case studies feature relevant behavioral topical areas: chronic disease prevention, community violence prevention, and educational intervention. To emphasize consistency with D\&I priorities, the discussion of the value of each method is framed around the elements of the established Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework. Systems science methods can help researchers, public health decision makers, and program implementers to understand the complex factors influencing successful D\&I of programs in community settings and to identify D\&I challenges imposed by system complexity.

Tags

Simulation Policy Model Network analysis Public-health Interventions