INNOVATIVE IMPLEMENTATION RESEARCH METHODS CORE

创新实施研究方法核心

• 批准号: 8189138
• 负责人: THOMAS W VALENTE
• 金额: $ 73.66万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8189138
• 关键词: Acquaintances; Address; Adoption; Affect; Behavior; Binding; Boxing; Characteristics; Collaborations; Communities; Complex; Computer Simulation; Computing Methodologies; Data; Data Analyses; Data Collection; Decision Making; Decision Support Systems; Drug abuse; Economics; Effectiveness; Elements; Engineering; Evaluation; Evidence based program; Feedback; Fertilization; Friends; Grant; Health behavior; Individual; Intervention; Interview; Knowledge; Lead; Left; Maps; Measurement; Measures; Methodology; Methods; Mission; Modeling; Monitor; Online Systems; Outcome; Pathway Analysis; Prevention; Prevention program; Preventive; Procedures; Process; Protocols documentation; Qualifying; Research; Research Methodology; Research Personnel; Resources; Right-On; Risk Behaviors; Science; Side; Simulate; Social Environment; Social Interaction; Social Network; Social support; Speed; Staging; System; Systems Integration; Techniques; Testing; Text; Time; Training; Translating; Work; abstracting; base; community organizations; computerized data processing; cost; design; implementation research; improved; innovation; insight; interest; models and simulation; next generation; programs; randomized trial; sex risk; social; success; theories; tool; tool development

. 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).

. 我们围绕图2左侧所示的综合方法研究和实践构建了这个核心。左手侧的三个椭圆形对应于本核心的三个子核心（见管理核心第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，忠诚度和可持续性。有证据表明，选择变革推动者的方式会影响项目成果[3]。因此，我们将与合格的赠款，以培训研究人员收集动态的社会网络信息，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侵入性最小的网络数据收集系统，以监控程序的实施，并提供反馈改进。这些系统工程工具是已经在其他领域成功使用的可靠方法[4，5]。计算建模提供的工具可以直接对实施过程中涉及的详细微观和宏观复杂干扰过程进行建模，这些过程实际上无法观察到，并从过程数据中抽象出意义（参见图1）。 测试提供了观察和实验方法来比较和改进实施策略。我们将整合这些方法进行表征，建模和测试，以比较预期与实际实施，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，提供专业知识和决策支持系统，例如资源和有效性工具（见第4.B.1节）。这些方法将被用来启动新的研究资格赠款和实践，关于如何最好地改善或加快采用循证方案，如何提高保真度或实施质量，以及如何维持，扩展或移动程序的规模。 最近，人们对预防工作中的社会网络分析重新产生了兴趣[6]，因为越来越多的人认识到健康和风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。这是我们工作中的一个重要主题。其次，评估社交网络随时间变化的方法很少，而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）。