. We have built this Core around integrative methods research and practice shown on the left of Figure 2. The three ovals on the left hand side correspond to the three subcores of this Core (see Administrative Core Section 4.A.5). A key insight is to understand how characteristics of the program implementers and their social relations affect adoption, fidelity, and sustainability. Evidence indicates the way in which change agents are selected can influence program outcomes [3]. Consequently we will work with qualifying grants to train researchers to collect dynamic social network information alongside implementation process and outcome data. We propose using systems engineering tools to design minimally intrusive network data collection systems to monitor program implementation, and provide feedback for improvement. These systems engineering tools are tried and true methods that have been used successfully in other fields [4, 5]. Computational modeling provides tools that directly model the detailed micro-level and macro-level complex interactional processes involved in implementation that cannot practically be observed and abstracts meaning from process data (see Figure 1). Testing provides observational and experimental approaches to compare and improve implementation strategies. We will integrate these methods for characterizing, modeling and testing to compare intended with actual implementation and will provide options based on empirical data and simulation modeling to advance the theory and practice of implementation. We will work closely with practice in providing expertise and decision support systems, such as the Resource and Effectiveness Tool (see Section 4.B.1). These methods will be used to initiate new research in qualifying grants and practice regarding how best to improve or quicken the adoption of evidence-based programs, how to improve fidelity or the quality of implementation, and how to sustain, extend or move programs to scale. Recently, there has been renewed interest in social network analysis for prevention work[6] because of a growing recognition that both health and risk behaviors are both heavily influenced by friends and acquaintances. However, there are few studies that have focused on how social networks influence implementation. This is a major theme in our work. Secondly, there is a paucity of methods for evaluating changes in social networks over time, and these so-called dynamic social networks are of fundamental importance to the implementation process (see Section 3.D.2).

National Institute of Health (NIH)
National Institute on Drug Abuse (NIDA)
Center Core Grants (P30)
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Special Emphasis Panel (ZDA1-EXL-T)
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Northwestern University at Chicago
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