INNOVATIVE IMPLEMENTATION RESEARCH METHODS CORE

。 我们围绕图2左侧所示的综合方法研究和实践构建了此核心。左侧的三个椭圆形对应于此核心的三个子核(请参阅管理核心第4.A.5节)。一个关键的洞察力是了解计划实施者的特征及其社会关系如何影响采用率、忠诚度和可持续性。有证据表明，选择变革推动者的方式可以影响项目结果[3]。因此，我们将与合格的赠款合作，培训研究人员收集动态社交网络信息以及实施过程和结果数据。我们建议使用系统工程工具来设计入侵最小的网络数据采集系统，以监控程序的执行，并提供改进的反馈。这些系统工程工具是已经在其他领域成功使用的可靠的方法[4，5]。计算建模提供了直接对实现中涉及的详细的微观级别和宏观级别的复杂交互流程进行建模的工具，这些流程实际上无法观察到，并从流程数据中提取意义(参见图1)。 测试提供了观察性和实验性的方法来比较和改进实现策略。我们将整合这些用于表征、建模和测试的方法，将预期与实际实施进行比较，并将提供基于经验数据和模拟建模的选项，以推进实施的理论和实践。我们将与实践密切合作，提供专门知识和决策支持系统，如资源和有效性工具(见第4.B.1节)。这些方法将被用于启动合格赠款和实践方面的新研究，涉及如何最好地改进或加快采用循证计划，如何提高保真度或实施质量，以及如何维持、扩大或推动计划的规模。 最近，人们对预防工作的社会网络分析重新产生了兴趣[6]，因为人们越来越认识到，健康和危险行为都受到朋友和熟人的严重影响。然而，很少有研究关注社交网络如何影响实施。这是我们工作的一个重大主题。其次，缺乏评估社交网络随时间变化的方法，这些所谓的动态社交网络对实施过程至关重要(见第3.D.2节)。

. 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).