参与式逻辑建模在一个多站点的倡议中,以推进实施科学
Douglas V Easterling1, Rebekah R Jacob2, Ross C Brownson2,3
1Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA. dveaster@wakehealth.edu.
参与式逻辑建模,涉及授予者开发程序逻辑模型,增强买入,并改善评估设计和多站点倡议的程序策略. 这种协作方法通过结合各种见解和澄清期望,有利于资助者,资助者和评估者.
科学领域:
- 项目评估 项目评估
- 实施科学 实施科学
- 健康倡议 卫生倡议
背景情况:
- 逻辑模型是关键的评估工具,可以映射程序的结果.
- 资助者鼓励逻辑模型,但往往排除资助人的发展.
- 参与式逻辑建模越来越被认为是有效的程序评估的必要条件.
研究的目的:
- 描述一个美国资助者对资助人的参与,以开发一个倡议的逻辑模型.
- 突出参与式逻辑建模在多站点环境中的好处.
- 检查国家癌症研究所实施癌症控制科学中心 (ISC3) 倡议的情况.
主要方法:
- 使用了一种反射的案例研究方法.
- 来自七个资助中心的代表共同构建了这项研究.
- 跨中心评估 (CCE) 工作组记录了逻辑模型开发和改进过程.
主要成果:
- 受助人的投入大大改变了最初的ISC3逻辑模型.
- 真正的参与促进了强大的赠款人购买和逻辑模型的使用.
- 受资助的中心根据逻辑模型调整了他们的评估设计和程序策略.
结论:
- 参与式逻辑建模在多站点倡议中为资助者,资助者和评估者提供了互惠的好处.
- 获奖者提供了有关可行性,所需资源和影响成功的上下文因素的宝贵见解.
- 协作逻辑模型开发提高了资助人对资助人的期望的理解,改善了对齐和结果.
更多相关视频
11:21Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
06:05The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
相关概念视频
Community Based Intervention
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...
Models of Health Promotion and Illness Prevention II
The agent-host-environment model states that disease results...
Models of Health Promotion and Illness Prevention I
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
Nursing Implementation
The five steps to implementing effective nursing care include reassessing the patient, reviewing and revising the existing nursing care plan, organizing the resources and care delivery, anticipating and preventing complications, and implementing nursing interventions.
Models, Theories, and Laws
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
