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利用社区参与实施基于证据的阿片类药物使用障碍治疗实践:一种数据驱动的范例和系统科学方法。

Using community engagement to implement evidence-based practices for opioid use disorder: A data-driven paradigm & systems science approach.

机构信息

School of Social Work, Columbia University, New York, USA.

School of Social Work, Columbia University, New York, USA.

出版信息

Drug Alcohol Depend. 2021 May 1;222:108675. doi: 10.1016/j.drugalcdep.2021.108675. Epub 2021 Mar 18.

DOI:10.1016/j.drugalcdep.2021.108675
PMID:33757707
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8058324/
Abstract

Community-driven responses are essential to ensure the adoption, reach and sustainability of evidence-based practices (EBPs) to prevent new cases of opioid use disorder (OUD) and reduce fatal and non-fatal overdoses. Most organizational approaches for selecting and implementing EBPs remain top-down and individually oriented without community engagement (CE). Moreover, few CE approaches have leveraged systems science to integrate community resources, values and priorities. This paper provides a novel CE paradigm that utilizes a data-driven and systems science approach; describes the composition, functions, and roles of researchers in CE; discusses unique ethical considerations that are particularly salient to CE research; and provides a description of how systems science and data-driven approaches to CE may be employed to select a range of EBPs that collectively address community needs. Finally, we conclude with scientific recommendations for the use of CE in research. Greater investment in CE research is needed to ensure contextual, equitable, and sustainable access to EBPs, such as medications for OUD (MOUD) in communities heavily impacted by the opioid epidemic. A data-driven approach to CE research guided by systems science has the potential to ensure adequate saturation and sustainability of EBPs that could significantly reduce opioid overdose and health inequities across the US.

摘要

社区驱动的应对措施对于确保采用、普及和维持基于证据的实践(EBP)以预防新的阿片类药物使用障碍(OUD)病例和减少致命和非致命性过量至关重要。大多数选择和实施 EBP 的组织方法仍然是自上而下的,以个人为导向,没有社区参与(CE)。此外,很少有 CE 方法利用系统科学来整合社区资源、价值观和优先事项。本文提供了一种新颖的 CE 范式,利用数据驱动和系统科学方法;描述了 CE 中研究人员的组成、功能和角色;讨论了 CE 研究中特别突出的独特伦理考虑因素;并描述了如何利用 CE 中的系统科学和数据驱动方法来选择一系列 EBP,这些 EBP 共同解决社区需求。最后,我们以科学的建议结束了 CE 在研究中的应用。需要加大对 CE 研究的投资,以确保在受阿片类药物流行严重影响的社区中获得基于证据的实践,如阿片类药物使用障碍(MOUD)药物,具有背景、公平和可持续性。由系统科学指导的 CE 研究的一种数据驱动方法有可能确保 EBP 的充分饱和和可持续性,这可能会显著减少美国的阿片类药物过量和健康不平等现象。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35bc/8058324/762410353275/nihms-1684969-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35bc/8058324/762410353275/nihms-1684969-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35bc/8058324/762410353275/nihms-1684969-f0001.jpg

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