System Dynamics of Cognitive Vulnerabilities and Family Support Among Latina Children and Adolescents

The paper describes an approach to developing a data-driven development of a feedback theory of cognitive vulnerabilities and family support focused on understanding the dynamics experienced among Latina children, adolescents, and families. Family support is understood to be a response to avoidant and maladaptive behaviors that may be characteristic of cognitive vulnerabilities commonly associated depression and suicidal ideation. A formal feedback theory is developed, appraised, and analyzed using a combination of secondary analysis of qualitative interviews (N = 30) and quantitative analysis using system dynamics modeling and simulation. Implications for prevention practice, treatment, and future research are discussed

Design and methods of Shape Up Under 5: Integration of systems science and community-engaged research to prevent early childhood obesity

Shape Up Under 5 (SUU5) was a two-year early childhood obesity prevention pilot study in Somerville, Massachusetts (2015–2017) designed to test a novel conceptual framework called Stakeholder-driven Community Diffusion. For whole-of-community interventions, this framework posits that diffusion of stakeholders’ knowledge about and engagement with childhood obesity prevention efforts through their social networks will improve the implementation of health-promoting policy and practice changes intended to reduce obesity risk. SUU5 used systems science methods (agent-based modeling, group model building, social network analysis) to design, facilitate, and evaluate the work of 16 multisector stakeholders (‘the Committee’). In this paper, we describe the design and methods of SUU5 using the conceptual framework: the approach to data collection, and methods and rationale for study inputs, activities and evaluation, which together may further our understanding of the hypothesized processes within Stakeholder-driven Community Diffusion. We also present a generalizable conceptual framework for addressing childhood obesity and similar complex public health issues through whole-of-community interventions

Outcomes for Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda

An unresolved issue in the field of implementation research is how to conceptualize and evaluate successful implementation. This paper advances the concept of “implementation outcomes” distinct from service system and clinical treatment outcomes. This paper proposes a heuristic, working “taxonomy” of eight conceptually distinct implementation outcomes—acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability—along with their nominal definitions. We propose a two-pronged agenda for research on implementation outcomes. Conceptualizing and measuring implementation outcomes will advance understanding of implementation processes, enhance efficiency in implementation research, and pave the way for studies of the comparative effectiveness of implementation strategies

Implementation of Evidence-Based Practice in Community Behavioral Health: Agency Director Perspectives

Despite a growing supply of evidence-based mental health treatments, we have little evidence about how to implement them in real-world care. This qualitative pilot study captured the perspectives of agency directors on the challenge of implementing evidence-based practices in community mental health agencies. Directors identified challenges as limited access to research, provider resistance, and training costs. Director leadership, support to providers, and partnerships with universities were leverage points to implement evidenced-based treatments. Directors’ mental models of EBP invoked such concepts as agency reputation, financial solvency, and market niche. Findings have potential to shape implementation interventions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61161/1/Implementation.pd

Scaling-up impact in perinatology through systems science: Bridging the collaboration and translational divides in cross-disciplinary research and public policy

© 2015 Elsevier Inc.. Despite progress over the past decade in reducing the global burden of newborn deaths, gaps in the knowledge base persist, and means of translating empirical findings into effective policies and programs that deliver life-saving interventions remain poorly understood. Articles in this issue highlight the relevance of transdisciplinary research in perinatology and calls for increased efforts to translate research into public policy and to integrate interventions into existing primary care delivery systems. Given the complexity and multi-causality of many of the remaining challenges in newborn health, and the effects that social and economic factors have over many newborn conditions, it has further been proposed that integrated, multi-sector public policies are also required. In this article, we discuss the application of systems science methods to advance transdisciplinary research and public policy-making in perinatology. Such approaches to research and public policy have been used to address various global challenges but have rarely been implemented in developing country settings. We propose that they hold great promise to improve not only our understanding of complex perinatology problems but can also help translate research-based insights into effective, multi-pronged solutions that deliver positive, intended effects. Examples of successful transdisciplinary science exist, but successes and failures are context specific, and there are no universal blueprints or formulae to reproduce what works in a specific context into different social system settings. Group model building is a tool, based in the field of System Dynamics, that we have used to facilitate transdisciplinary research and, to a lesser extent, policy formulation in a systematic and replicable way. In this article, we describe how group model building can be used and argue for scaling its use to further the translation of empirical evidence and insights into policy and action that increase maternal and neonatal survival and well-being