INNOVATIVE IMPLEMENTATION RESEARCH METHODS CORE

创新实施研究方法核心

基本信息

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
项目类别：
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

项目摘要

. 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).

。我们已经围绕图2的左侧所示的集成方法研究和实践建立了此核心。左侧的三个椭圆形对应于该核心的三个子核（请参阅第4.a.5节的管理核心第4.a.5节）。一个关键的见解是了解计划实施者的特征及其社会关系如何影响采用，忠诚和可持续性。证据表明选择变更代理的方式可以影响计划结果[3]。因此，我们将与合格的赠款一起培训研究人员，以收集动态的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设计最小侵入性的网络数据收集系统来监视程序实现，并提供改进的反馈。这些系统工程工具是尝试的，并且在其他领域成功使用了真实的方法[4，5]。计算建模提供了直接建模的工具，可以直接建模实现的详细的微级和宏观复杂的交互过程，这些过程实际上无法观察到，并从过程数据中抽象含义（见图1）。测试提供了比较和改善实施策略的观察和实验方法。我们将整合这些方法以表征，建模和测试，以将目的与实际实施进行比较，并根据经验数据和仿真建模提供选项，以推动实施的理论和实践。我们将与实践紧密合作，以提供专业知识和决策支持系统，例如资源和有效性工具（请参阅第4.B.1节）。这些方法将用于启动有关如何最好地改善或加快采用循证计划，如何提高忠诚度或实施质量以及如何维持，扩展或移动扩展计划进行扩展的新研究。最近，人们对预防工作的社交网络分析有了重新兴趣[6]，因为人们越来越认识到健康和风险行为都受到朋友和熟人的影响。但是，很少有研究重点是社交网络如何影响实施。这是我们工作的主要主题。其次，随着时间的推移，可以评估社交网络变化的方法很少，而这些所谓的动态社交网络对于实施过程至关重要（请参阅第3.D.2节）。