Suppr超能文献

增强行为干预科学:运用基于社区的参与式研究原则与多阶段最优化策略。

Enhancing behavioral intervention science: using community-based participatory research principles with the multiphase optimization strategy.

机构信息

School of Social Work, The University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

North Jersey Community Research Initiative, Newark, NJ 07103, USA.

出版信息

Transl Behav Med. 2021 Aug 13;11(8):1596-1605. doi: 10.1093/tbm/ibab032.

Abstract

Innovative methodological frameworks are needed in intervention science to increase efficiency, potency, and community adoption of behavioral health interventions, as it currently takes 17 years and millions of dollars to test and disseminate interventions. The multiphase optimization strategy (MOST) for developing behavioral interventions was designed to optimize efficiency, efficacy, and sustainability, while community-based participatory research (CBPR) engages community members in all research steps. Classical approaches for developing behavioral interventions include testing against control interventions in randomized controlled trials. MOST adds an optimization phase to assess performance of individual intervention components and their interactions on outcomes. This information is used to engineer interventions that meet specific optimization criteria focused on effectiveness, cost, or time. Combining CBPR and MOST facilitates development of behavioral interventions that effectively address complex health challenges, are acceptable to communities, and sustainable by maximizing resources, building community capacity and acceptance. Herein, we present a case study to illustrate the value of combining MOST and CBPR to optimize a multilevel intervention for reducing substance misuse among formerly incarcerated men, for under $250 per person. This integration merged experiential and cutting-edge scientific knowledge and methods, built community capacity, and promoted the development of efficient interventions. Integrating CBPR and MOST principles yielded a framework of intervention development/testing that is more efficient, faster, cheaper, and rigorous than traditional stage models. Combining MOST and CBPR addressed significant intervention science gaps and speeds up testing and implementation of interventions.

摘要

创新的方法学框架在干预科学中是必要的,以提高行为健康干预的效率、效力和社区采用率,因为目前测试和传播干预措施需要 17 年和数百万美元。多阶段最优策略(MOST)是为了优化效率、效果和可持续性而设计的,而社区参与式研究(CBPR)则使社区成员参与所有研究步骤。经典的行为干预开发方法包括在随机对照试验中对对照干预进行测试。MOST 增加了一个优化阶段,以评估单个干预组件及其对结果的相互作用的性能。这些信息用于设计符合特定优化标准的干预措施,这些标准侧重于有效性、成本或时间。将 CBPR 和 MOST 相结合,有助于开发能够有效应对复杂健康挑战、为社区所接受且可持续的行为干预措施,方法是最大限度地利用资源、建立社区能力和接受度。在此,我们提出了一个案例研究,说明将 MOST 和 CBPR 相结合以优化一项针对减少前囚犯药物滥用的多层次干预措施的价值,每人成本不到 250 美元。这种整合融合了经验和前沿的科学知识和方法,建立了社区能力,并促进了高效干预措施的发展。将 CBPR 和 MOST 原则相结合,产生了一种干预开发/测试的框架,比传统的阶段模型更高效、更快、更便宜且更严格。将 MOST 和 CBPR 相结合解决了干预科学中的重大差距,并加快了干预措施的测试和实施。

相似文献

4
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.

引用本文的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验