从COVID-19对低收入和中等收入国家政策决策的建模努力中吸取的经验教训
Collins J Owek1, Fatuma Hassan Guleid2, Justinah Maluni2
1Department of Public and Global Health, University of Nairobi, Nairobi, Kenya.
BMJ global health
|November 9, 2024
概括
从数学建模中进行有效的知识转换需要能力建设,更好的数据基础设施和专用平台. 强大的研究人员与政策制定者关系和政策问题的联合制作是公共卫生紧急情况期间基于证据的决策的关键推动因素.
科学领域:
- 公共卫生 公共卫生
- 卫生政策 卫生政策
- 流行病学建模 流行病学建模
背景情况:
- "COVID-19"大流行突显出严重的健康和社会经济影响,受政策决策的影响.
- 有限的证据存在于指导政策决策在设置有限制的疫情前建模能力的设置.
研究的目的:
- 确定知识转化机制,支持因素和结构,以便有效地将建模证据转化为政策决策.
- 了解COVID-19大流行期间低收入和中等收入国家 (LMICs) 证据转化为政策的挑战和促进者.
主要方法:
- 融合混合方法,结合参与式行动方法.
- 定量调查数据和从范围审查,采访和研讨会笔记中获得的定性数据.
- 参与了来自非洲,东南亚和拉丁美洲的LMIC的研究人员和政策参与者.
主要成果:
- 有效使用建模证据的策略包括建模和通信能力建设,增强数据基础设施,持续融资和知识翻译平台.
- 关键的推动因素包括研究人员与政策制定者之间强有力的关系,信誉,政策问题的联合制作以及研究人员融入政策制定.
- 障碍包括模拟器之间的竞争,政策制定者对研究的负面态度,政治影响以及对快速成果的需求.
结论:
- 在COVID-19大流行期间,为LMICs实现了对知识翻译的上下文理解.
- 学到的经验教导了疫情准备和长期投资,将证据转化为政策.
- 一个共同开发的知识翻译框架可以指导公共卫生紧急情况的决策.
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