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作 者:孔凡懿 牛佳苗 刘缘 段晖[2] KONG Fanyi(Institute for Hospital Management of Tsinghua University,Shenzhen,Guangdong,518055,China)
机构地区:[1]清华大学医院管理研究院,广东深圳518055 [2]中国人民大学公共管理学院,北京100872
出 处:《医学与社会》2025年第3期32-38,共7页Medicine and Society
基 金:国家社会科学基金资助重大项目,编号为23ZD080。
摘 要:目的:识别我国突发公共卫生事件应急治理的驱动因素,为不同地区精准优化应急治理、有效应对突发公共卫生事件提供参考。方法:基于4R理论筛选变量,通过模糊集定性比较分析(fsQCA)进行必要性分析与标准化分析。结果:必要条件分析表明,影响东西部地区突发公共卫生事件应急治理的必要条件是数字智能水平,中部地区缺乏必要条件。条件组态分析表明,每个地区各存在两条高水平组态路径,总体一致性分别为0.897、0.802和0.997,总案例覆盖度依次是69%、74%和83%。模型解释程度高。结论:就异质性而言,东中西部的实现模式可归纳为“数字智能”模式、“智能—资源”模式与“基建—智能”模式。就同质性而言,科技时代的突发公共卫生事件应急治理由数字智能水平驱动,数智技术具备全生命周期赋能效应。因此,突发公共卫生事件应急需要回归治理的本源价值,各地区应因地制宜采取技术嵌入、资源调配、基建完善、资金支持等措施。Objective:This study identified driving factors of public health emergency governance,so as to provide a reference for different regions to accurately improve emergency governance and effectively respond to public health emergency.Methods:A variable selection framework was built based on 4R theory.Necessity and standardization analysis was carried out through fuzzy set qualitative comparative analysis.Results:Necessary condition analysis revealed digital intelligence was a pivotal factor for the emergency governance in eastern and western regions,whereas central region was deficient in this driving factor.High-level configurations found two paths existed in each region with an overall consistency of 0.897,0.802 and 0.997,which could cover 69%,74%and 83%of cases respectively.The model had high explanatory power.Conclusion:In terms of heterogeneity,implementation models in the East,Central and West can be summarized as"Digital Intelligence","Intelligence-Resource",and"Infrastructure-Intelligence"respectively.In terms of homogeneity,public health emergency governance in technology era is driven by digital intelligence.Digital technology has whole life cycle empowerment effect.Therefore,public health emergency needs to return to the original value of governance.Every region should adopt technological embedding,resource deployment,infrastructure improvement and financial support measures.
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