Exploring the Integration of Systems and Social Sciences to Study Evidence Use among Child Welfare Policy-Makers

By Mackie, Thomas I.; Sheldrick, R. Christopher et al. | Child Welfare, May 1, 2015 | Go to article overview

Exploring the Integration of Systems and Social Sciences to Study Evidence Use among Child Welfare Policy-Makers


Mackie, Thomas I., Sheldrick, R. Christopher, Hyde, Justeen, Leslie, Laurel K., Child Welfare


Both academic research and federal investigations draw attention to the chasm between child welfare policy and available research evidence (Government Accountability Office [GAO], 2012; Ko et al., 2008). Linear models of policy adoption have been proposed in prior studies, based on the hypothesis that policymakers lack relevant research evidence either because such evidence has not yet been generated or because of inadequate dissemination of existing evidence to policymakers (Glisson & Green, 2006). We suggest an alternative approach, drawing on the capability of transactional frameworks to capture the complex and dynamic interplay between social networks and other influential sociopolitical factors, the availability of research evidence and other types of evidence, the policymaking process, and the decision-makers through application of systems science models (Oliver, Innvar, Lorenc, Woodman, & Thomas, 2014).

To develop a dynamic framework for research evidence use, we propose the use of interdisciplinary methodological approaches drawing from qualitative methods combined with systems engineering. Methods from systems engineering, an interdisciplinary field that focuses on the design and management of complex systems over the life cycle (Sterman, 2000), are increasingly employed in health and human services research. Even in the initial phases of research, these methods offer the opportunity to make theoretical assumptions explicit through operationalizing quantitative variables, linking theoretic models and empirical findings, and exploring the potential impact of interventions in simulated systems models. Combining qualitative methods from the social sciences with systems engineering methods from the systems sciences provides rich, textured informational inputs that further facilitate an in-depth explanation of concepts for incorporation into these models.

To illustrate this interdisciplinary approach, we draw on data from a case study that examines state policymakers' use of research evidence in response to federal mandates to develop a plan for psychotropic medication oversight for children in the U.S. foster care system (United States Department of Health and Human Services [DHHS] & Administration on Children, Youth, and Families [ACYF], 2012). In this case study, the available research evidence raised concerns regarding both the quality and safety of psychotropic medication use, including potential overuse of antipsychotic medications among young children and use of multiple classes of psychotropic medication simultaneously, without prior research (e.g., Rubin, Matone, Huang, dosReis, Feudtner, & Localio, 2012; Crystal et al., 2016). Some states had previously implemented psychotropic oversight mechanisms, but there was limited evidence on the effectiveness of different approaches (e.g., drug utilization review, prior authorization) (Stein et al., 2014; Center for Medicare and Medicaid Services [CMS], 2016). Presented with a compelling need to implement oversight but lacking a "gold standard" intervention with which to respond, psychotropic oversight presents policymakers with significant uncertainty on the scientific level, thus requiring reliance on evidence other than the "gold standard" of meta-analyses from randomized control trials (Hyde, Mackie, Palinkas, Niemi, & Leslie, 2015).

Our aims are ultimately twofold: (i.) to examine whether a dynamic, transactional model can help to explain policymakers use of research evidence where information about the effectiveness of potential policy responses is lacking and and (ii.) to investigate the interplay between policymaking, social networks, and evidence use employing rigorous qualitative methods. We begin with a discussion on diffusion of innovations, a theoretical construct common to both the social and systems sciences. We next describe our methods, including the use of both qualitative methods and a prominent systems dynamics model commonly employed in systems engineering, the Bass Diffusion Model, to our case study. …

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