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Article(id=1320438137772429466, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, articleNumber=null, orderNo=null, doi=10.11847/zgggws1146412, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1730563200000, receivedDateStr=2024-11-03, revisedDate=1756742400000, revisedDateStr=2025-09-02, acceptedDate=1761753600000, acceptedDateStr=2025-10-30, onlineDate=1792746584599, onlineDateStr=2026-10-23, pubDate=1775750400000, pubDateStr=2026-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792746584599, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792746584599, creator=13701087609, updateTime=1792746584599, updator=13701087609, issue=Issue{id=1320438090615862080, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='4', pageStart='385', pageEnd='512', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792746573356, creator='13701087609', updateTime=1792748076043, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320444393430040620, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320444393430040621, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=404, endPage=411, ext={EN=ArticleExt(id=1320438139437568155, articleId=1320438137772429466, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Influencing factors and optimization strategies of multi-subject synergistic effect of urban communities in response to public health emergencies based on fuzzy DANP, columnId=1320438091370836801, journalTitle=Chinese Journal of Public Health, columnName=Special Report—Development of Public Health Evaluation Indicator Frameworks, runingTitle=null, highlight=null, articleAbstract=
Objective

To clarify the key factors influencing the multi-subject synergistic effect of urban communities in response to public health emergencies and their interrelationships, thus enhancing the quality and efficiency of multi-subject collaboration in response to public health risks and providing references for the scientific and efficient work of urban communities under such conditions.

Methods

With the modified SFIC model as the theoretical framework, a system of influencing factors was established through literature research, semi-structured interviews, and the Delphi method. Fuzzy DANP was employed to clarify the influencing relationships among the factors and quantify their weights. The key influencing factors were identified with consideration to the centrality, causality, and weight ranking of the factors.

Results

A system of factors influencing the multi-subject synergistic effect of urban communities in response to public health emergencies was established, consisting of 5 primary factors, 15 secondary factors, and 47 tertiary factors. Among the primary factors, synergy engine, synergy supervision, and synergy environment were of higher importance. The key secondary factors included leadership, organizational structure, and ideological consensus. The main tertiary factors were the support strength of policies, laws, and regulations, operability of systems, accuracy of information, timeliness of resource acquisition, timeliness of information sharing, capability of personnel teams, and risk identification capabilities.

Conclusions

To enhance the capacity of urban communities in response to public health risks in a coordinated manner and promote the long-term development of synergistic effects, efforts should be made to strengthen the spport of synergistic policies, give full play to the leading role of leadership, foster a solid foundation of ideological consensus, and optimize the support of data and intelligence.

, authors=Yue WANG, Xiaohan SUN, Yunxia ZHONG, Jun LI, authorsList=Yue WANG, Xiaohan SUN, Yunxia ZHONG, Jun LI, authorCompany=null, correspAuthors=Jun LI, authorNote=null, correspAuthorsNote=
LI Jun, E-mail:
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目的

厘清影响城市社区卫生应急多主体协同效应的关键因素及其相互关系，提高多主体协同应对公共卫生风险的质量与效率，为科学、高效地开展城市社区卫生应急工作提供有益参考。

方法

以协同治理普适模型（SFIC）的修正模型为理论框架，结合文献研究法、半结构化访谈法和德尔菲专家咨询法构建影响因素体系；运用Fuzzy-DANP法明确各因素间的影响关系并量化其权重，结合因素的中心度、原因度和权重排序位次识别分析关键影响因素。

结果

构建了由5个一级因素、15个二级因素和47个三级因素组成的城市社区卫生应急多主体协同效应影响因素体系。其中，协同引擎、协同监督、协同环境在一级影响因素层面的重要性较高；领导能力、组织结构和思想共识等为关键二级影响因素；政策法规支持力度、制度可操作性、信息准确性、资源获取及时性、信息共享及时度、人员队伍能力水平、风险识别能力等为主要的三级影响因素。

结论

有效提升城市社区多主体协同应对公共卫生风险能力，促进协同效应的长期稳定发展，可从强化协同政策支撑、发挥领导权威引领作用、筑牢思想共识基础及优化数智支持等方面入手。

, authors=王悦, 孙笑菡, 钟韫霞, 李军*, *, authorsList=王悦, 孙笑菡, 钟韫霞, 李军, authorCompany=null, correspAuthors=李军, authorNote=

王悦（2000—），硕士，研究方向：公共卫生

, correspAuthorsNote=
李军，E-mail：
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TFN corresponding to linguistic terms

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语言变量专家打分对应的三角模糊数/TFN
无影响0（0.00，0.00，0.25）
影响非常小1（0.00，0.25，0.50）
影响较小2（0.25，0.50，0.75）
影响较大3（0.50，0.75，1.00）
影响非常大4（0.75，1.00，1.00）
), ArticleFig(id=1320438153245216974, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137772429466, language=CN, label=表1, caption=

语言变量的模糊尺度

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语言变量专家打分对应的三角模糊数/TFN
无影响0（0.00，0.00，0.25）
影响非常小1（0.00，0.25，0.50）
影响较小2（0.25，0.50，0.75）
影响较大3（0.50，0.75，1.00）
影响非常大4（0.75，1.00，1.00）
), ArticleFig(id=1320438153719173327, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137772429466, language=EN, label=Tab.2, caption=

System and weights of factors influencing the synergistic effect of multiple subjects in urban communities in response to public health emergencies

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一级因素权重二级因素权重三级因素权重
A　协同环境0.199A1　政策法规0.076A1-1　政策法规完善程度0.024
A1-2　政策法规支持力度0.026
A2　经济发展0.069A2-1　人均地方财政收入0.019
A3　社会文化0.065A3-1　社区凝聚力0.019
A3-2　基层党建水平0.024
A4　环境规划0.050A4-1　生态环境规划0.010
A4-2　生活环境规划0.014
B　协同基础0.196B1　资源配置0.063B1-1　卫生应急物资储备充足度0.022
B1-2　卫生应急设施及机构完备度0.021
B1-3　人员队伍能力水平0.025
B1-4　卫生应急资金投入水平0.024
B1-5　信息平台建设与应用程度0.022
B2　能力培育0.068B2-1　卫生应急专业培训0.020
B2-2　卫生应急联合演练0.023
B2-3　卫生应急文化宣传0.017
B2-4　既有协作经验0.020
B3　组织结构0.070B3-1　组织架构完备性0.023
B3-2　权责利明确程度0.024
B3-3　组织可塑性和适应性0.023
B3-4　协同程序规范性0.023
C　协同引擎0.212C1　领导能力0.079C1-1　风险识别能力0.024
C1-2　统筹协调能力0.023
C1-3　权力让渡水平0.022
C1-4　科学决策能力0.018
C2　制度设计0.072C2-1　开放参与性0.019
C2-2　公开透明性0.019
C2-3　可操作性0.026
C2-4　规范全面性0.019
C2-5　修订及时性0.020
C3　思想共识0.070C3-1　相互信任度0.024
C3-2　目标一致性0.023
C3-3　风险共担性0.023
C3-4　协同责任意识0.018
D　协同行为0.188D1　多元参与0.066D1-1　多主体参与度0.024
D1-2　沟通渠道有效性0.021
D2　信息共享0.065D2-1　信息共享及时度0.025
D2-2　信息共享的广度与深度0.019
D2-3　信息对称性0.021
D2-4　信息准确性0.026
D3　资源整合0.063D3-1　资源获取及时性0.025
D3-2　资源调度的合理性和灵活性0.020
D3-3　资源互补性0.017
E　协同监管0.204E1　绩效评价0.063E1-1　评价范围完整性0.020
E1-2　评价指标适宜性0.022
E2　过程监控0.061E2-1　监督方式多样性0.019
E2-2　主体对象全面性0.018
E2-3　监督反馈效率0.020
), ArticleFig(id=1320438154000191696, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137772429466, language=CN, label=表2, caption=

城市社区卫生应急多主体协同效应影响因素体系及权重

, figureFileSmall=null, figureFileBig=null, tableContent=
一级因素权重二级因素权重三级因素权重
A　协同环境0.199A1　政策法规0.076A1-1　政策法规完善程度0.024
A1-2　政策法规支持力度0.026
A2　经济发展0.069A2-1　人均地方财政收入0.019
A3　社会文化0.065A3-1　社区凝聚力0.019
A3-2　基层党建水平0.024
A4　环境规划0.050A4-1　生态环境规划0.010
A4-2　生活环境规划0.014
B　协同基础0.196B1　资源配置0.063B1-1　卫生应急物资储备充足度0.022
B1-2　卫生应急设施及机构完备度0.021
B1-3　人员队伍能力水平0.025
B1-4　卫生应急资金投入水平0.024
B1-5　信息平台建设与应用程度0.022
B2　能力培育0.068B2-1　卫生应急专业培训0.020
B2-2　卫生应急联合演练0.023
B2-3　卫生应急文化宣传0.017
B2-4　既有协作经验0.020
B3　组织结构0.070B3-1　组织架构完备性0.023
B3-2　权责利明确程度0.024
B3-3　组织可塑性和适应性0.023
B3-4　协同程序规范性0.023
C　协同引擎0.212C1　领导能力0.079C1-1　风险识别能力0.024
C1-2　统筹协调能力0.023
C1-3　权力让渡水平0.022
C1-4　科学决策能力0.018
C2　制度设计0.072C2-1　开放参与性0.019
C2-2　公开透明性0.019
C2-3　可操作性0.026
C2-4　规范全面性0.019
C2-5　修订及时性0.020
C3　思想共识0.070C3-1　相互信任度0.024
C3-2　目标一致性0.023
C3-3　风险共担性0.023
C3-4　协同责任意识0.018
D　协同行为0.188D1　多元参与0.066D1-1　多主体参与度0.024
D1-2　沟通渠道有效性0.021
D2　信息共享0.065D2-1　信息共享及时度0.025
D2-2　信息共享的广度与深度0.019
D2-3　信息对称性0.021
D2-4　信息准确性0.026
D3　资源整合0.063D3-1　资源获取及时性0.025
D3-2　资源调度的合理性和灵活性0.020
D3-3　资源互补性0.017
E　协同监管0.204E1　绩效评价0.063E1-1　评价范围完整性0.020
E1-2　评价指标适宜性0.022
E2　过程监控0.061E2-1　监督方式多样性0.019
E2-2　主体对象全面性0.018
E2-3　监督反馈效率0.020
), ArticleFig(id=1320438154079883473, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137772429466, language=EN, label=Tab.3, caption=

Comprehensive influence degrees and rankings of primary and secondary influencing factors

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一级因素Ri/排名Di/排名Ri+Di/排名Ri–Di/排名二级因素Ri/排名Di/排名Ri+Di/排名Ri−Di/排名因素属性
A12.42/310.85/523.27/51.57/1A15.94/23.96/149.90/111.98/1原因因素
A25.38/53.88/159.26/141.50/2原因因素
A35.06/104.51/139.57/130.55/4原因因素
A43.89/154.80/128.69/15−0.91/13结果因素
B12.20/412.73/324.93/3−0.53/4B14.93/125.66/410.59/9−0.73/12结果因素
B25.34/65.56/510.90/6−0.22/8结果因素
B35.49/75.42/810.91/50.07/6原因因素
C13.24/112.83/226.07/10.41/3C16.14/15.29/911.43/10.85/3原因因素
C25.60/35.50/611.10/30.10/5原因因素
C35.50/45.44/710.94/40.06/7原因因素
D11.70/514.14/125.84/2−2.44/5D15.13/86.07/111.20/2−0.94/14结果因素
D25.10/95.79/310.89/7−0.69/11结果因素
D34.89/135.99/210.88/8−1.10/15结果因素
E12.73/211.74/424.48/40.99/2E14.94/115.20/1010.14/10−0.26/10结果因素
E24.81/145.06/119.87/12−0.25/9结果因素
), ArticleFig(id=1320438154335736018, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137772429466, language=CN, label=表3, caption=

一、二级影响因素的综合影响指数及排名

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一级因素Ri/排名Di/排名Ri+Di/排名Ri–Di/排名二级因素Ri/排名Di/排名Ri+Di/排名Ri−Di/排名因素属性
A12.42/310.85/523.27/51.57/1A15.94/23.96/149.90/111.98/1原因因素
A25.38/53.88/159.26/141.50/2原因因素
A35.06/104.51/139.57/130.55/4原因因素
A43.89/154.80/128.69/15−0.91/13结果因素
B12.20/412.73/324.93/3−0.53/4B14.93/125.66/410.59/9−0.73/12结果因素
B25.34/65.56/510.90/6−0.22/8结果因素
B35.49/75.42/810.91/50.07/6原因因素
C13.24/112.83/226.07/10.41/3C16.14/15.29/911.43/10.85/3原因因素
C25.60/35.50/611.10/30.10/5原因因素
C35.50/45.44/710.94/40.06/7原因因素
D11.70/514.14/125.84/2−2.44/5D15.13/86.07/111.20/2−0.94/14结果因素
D25.10/95.79/310.89/7−0.69/11结果因素
D34.89/135.99/210.88/8−1.10/15结果因素
E12.73/211.74/424.48/40.99/2E14.94/115.20/1010.14/10−0.26/10结果因素
E24.81/145.06/119.87/12−0.25/9结果因素
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城市社区卫生应急多主体协同效应影响因素及优化策略：基于Fuzzy-DANP法
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王悦 , 孙笑菡 , 钟韫霞 , 李军 *, *
中国公共卫生 | 专题报道——公共卫生评价指标体系构建 2026,42(4): 404-411
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中国公共卫生 |专题报道——公共卫生评价指标体系构建 2026 , 42 (4) : 404 -411
城市社区卫生应急多主体协同效应影响因素及优化策略：基于Fuzzy-DANP法
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王悦, 孙笑菡, 钟韫霞, 李军*, *
作者信息
  • 首都医科大学公共卫生学院，北京 100069
通讯作者:
李军，E-mail：
作者简介:

王悦（2000—），硕士，研究方向：公共卫生

Influencing factors and optimization strategies of multi-subject synergistic effect of urban communities in response to public health emergencies based on fuzzy DANP
Yue WANG, Xiaohan SUN, Yunxia ZHONG, Jun LI
Affiliations
  • School of Public Health, Capital Medical University, Beijing 100069, China
    • Corresponding Author:
    LI Jun, E-mail:
出版时间: 2026-04-10 doi: 10.11847/zgggws1146412
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摘要
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目的

厘清影响城市社区卫生应急多主体协同效应的关键因素及其相互关系，提高多主体协同应对公共卫生风险的质量与效率，为科学、高效地开展城市社区卫生应急工作提供有益参考。

方法

以协同治理普适模型（SFIC）的修正模型为理论框架，结合文献研究法、半结构化访谈法和德尔菲专家咨询法构建影响因素体系；运用Fuzzy-DANP法明确各因素间的影响关系并量化其权重，结合因素的中心度、原因度和权重排序位次识别分析关键影响因素。

结果

构建了由5个一级因素、15个二级因素和47个三级因素组成的城市社区卫生应急多主体协同效应影响因素体系。其中，协同引擎、协同监督、协同环境在一级影响因素层面的重要性较高；领导能力、组织结构和思想共识等为关键二级影响因素；政策法规支持力度、制度可操作性、信息准确性、资源获取及时性、信息共享及时度、人员队伍能力水平、风险识别能力等为主要的三级影响因素。

结论

有效提升城市社区多主体协同应对公共卫生风险能力，促进协同效应的长期稳定发展，可从强化协同政策支撑、发挥领导权威引领作用、筑牢思想共识基础及优化数智支持等方面入手。

卫生应急  /  城市社区  /  协同效应  /  Fuzzy-DANP  /  影响因素
Objective

To clarify the key factors influencing the multi-subject synergistic effect of urban communities in response to public health emergencies and their interrelationships, thus enhancing the quality and efficiency of multi-subject collaboration in response to public health risks and providing references for the scientific and efficient work of urban communities under such conditions.

Methods

With the modified SFIC model as the theoretical framework, a system of influencing factors was established through literature research, semi-structured interviews, and the Delphi method. Fuzzy DANP was employed to clarify the influencing relationships among the factors and quantify their weights. The key influencing factors were identified with consideration to the centrality, causality, and weight ranking of the factors.

Results

A system of factors influencing the multi-subject synergistic effect of urban communities in response to public health emergencies was established, consisting of 5 primary factors, 15 secondary factors, and 47 tertiary factors. Among the primary factors, synergy engine, synergy supervision, and synergy environment were of higher importance. The key secondary factors included leadership, organizational structure, and ideological consensus. The main tertiary factors were the support strength of policies, laws, and regulations, operability of systems, accuracy of information, timeliness of resource acquisition, timeliness of information sharing, capability of personnel teams, and risk identification capabilities.

Conclusions

To enhance the capacity of urban communities in response to public health risks in a coordinated manner and promote the long-term development of synergistic effects, efforts should be made to strengthen the spport of synergistic policies, give full play to the leading role of leadership, foster a solid foundation of ideological consensus, and optimize the support of data and intelligence.

response to public health emergency  /  urban community  /  synergistic effect  /  fuzzy DANP  /  influencing factor
王悦, 孙笑菡, 钟韫霞, 李军. 城市社区卫生应急多主体协同效应影响因素及优化策略：基于Fuzzy-DANP法. 中国公共卫生, 2026 , 42 (4) : 404 -411 . DOI: 10.11847/zgggws1146412
Yue WANG, Xiaohan SUN, Yunxia ZHONG, Jun LI. Influencing factors and optimization strategies of multi-subject synergistic effect of urban communities in response to public health emergencies based on fuzzy DANP[J]. Chinese Journal of Public Health, 2026 , 42 (4) : 404 -411 . DOI: 10.11847/zgggws1146412
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面对新发突发公共卫生危机，城市社区作为抵御各类风险冲击的第一道防线，在应急治理中的重要性愈发凸显[1]，预防为主、多元协同与网格化模式成为城市社区公共卫生应急管理的必然趋势[2]。目前，多元主体联防联控的社区应急格局已初步形成[3]，但受多部门职责不清、协同意识不足、监督缺乏、信息壁垒等多种因素的制约[4–5]，协同实践中仍面临协同效率低下和协同效果不佳等“协同惰性”困局[6]，难以实现“1+1>2”的协同效应。因此，厘清影响城市社区卫生应急多主体协同效应的各方面因素及其相互关系，是有效消解协同惰性、切实提高城市社区应急能力的关键。近年来，围绕社区应急协同治理，国内外学者从体系框架构建[7–8]、协同困境与优化路径[3,9–10]、协同机制与模式探索以及影响因素分析等多方面进行了研究[11–15]。但这些研究主要为宏观层面的理论探讨和案例分析，从全流程、全要素角度对多主体协同影响因素的研究尚显不足，尤其缺乏对复杂因素间影响关系的定量分析。鉴于此，本研究从城市社区卫生应急多主体协同与联动的全周期过程出发，综合运用定性与定量研究方法，系统识别并分析影响协同效应的关键因素及其相互关系，提出针对性的优化策略，为今后科学、精准、高效地开展城市社区突发公共卫生事件防控工作提供参考借鉴。
1　资料与方法
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1.1　资料来源
以文献检索与半结构化访谈的方式收集相关资料与信息。（1）在中国知网、维普、万方、PubMed和Web of Science等中英文数据库，以滚雪球方式，通过文献关键词、作者及文内相关参考文献等多维度检索文献，归纳整理影响协同效应的相关因素。（2）2024年5—6月，根据纳入标准（从事社区卫生应急相关工作、具有至少5年以上的相关工作经验、担任管理或决策层面的职位、对社区应急管理有深刻理解和直接参与），遴选5名专家，围绕城市社区卫生应急多主体协同与联动效果的影响因素及提升策略等议题进行了半结构化访谈，进一步深入识别影响因素。最后，在SFIC修正模型框架下，构建出包含5个一级因素、14个二级因素和48个三级因素的城市社区卫生应急多主体协同效应影响因素体系。本研究已获得首都医科大学医学伦理委员会批准（编号：Z2023SY096）。为保证所选影响因素的科学合理性，并探究主要影响因素，本研究采用以下线路开展研究工作，见图1。
1.2　理论框架构建
1.2.1　SFIC模型及其适用性
SFIC模型是由美国学者Ansell和Gash构建的协同治理普适模型[16]，包括起始条件（starting conditions）、催化领导（facilitative leadership）、制度设计（institutional design）和协同过程（collaborative process）4个关键要素。与其他协同治理模型相比[17–20]，SFIC模型考虑到协同过程中各阶段影响因素的权变性和非线性关系，以其环形结构展现出多元主体互动和博弈的复杂性，能更全面地解释协同过程的整体效应，已被广泛应用于医疗卫生领域协同发展的多个不同主题中[21–24]。城市社区卫生应急多主体协同与联动，是指政府各职能部门、街道办事处、社区居委会、社区卫生服务中心、物业企业、社会组织等通力合作，以有效防范和应对各类突发公共卫生事件、保护公众健康为核心目标，以相关法律法规和卫生应急预案为指导，依托多源客体资源和技术工具，促进资源共享、信息互通、行动协调和优势互补，最终实现协同效应的动态复杂过程[25]。协同效应理论指出，多个主体间的有效合作可产生超越单个主体能力总和的效果[26]。当城市社区卫生应急各参与主体间达成良好合作秩序与博弈均衡状态时，“1+1>2”的协同效应得以体现，如应急响应质量和效率的提升、各类资源利用的最大化、应急资金和人力等成本投入减少，以及事件带来的损失与健康风险降低等。这与SFIC模型所体现的多主体不断进行多维协作产生协同效果的协同逻辑相契合。
1.2.2　SFIC模型修正
针对城市社区卫生应急与多主体协同联动这一具体问题，本研究通过文献分析，并结合专家访谈的意见，对SFIC模型进行适应性修正：其一，由于原有模型未能反映外部环境变化对多元主体协同应急产生的影响，故引入“协同环境”要素，以强调政策法规、经济发展和社会文化等外部因素对系统的影响。其二，参考SFIC模型的完善方案[27]，将原模型中复杂的协同过程简化为协同引擎和协同行为两部分。其三，考虑到有效的监督和评估是维系协同效果、确保协同过程有序性的重要举措，故引入“协同监督”模块，以实现对协同过程与效果的评估、监管与反馈作用。综上，本研究从协同环境、协同基础、协同引擎、协同行为以及协同监督5个维度构建了理论框架（图2）。
1.3　研究方法
1.3.1　德尔菲专家咨询法
共邀请17名在卫生应急相关领域具有丰富理论与实践经验的专家参与咨询，通过电子邮件发放和回收问卷。问卷内容主要包括：（1）专家影响因素重要性评分表及意见栏；（2）专家判断依据（Ca）和熟悉程度（Cs）；（3）专家基本信息。采用界值法筛选影响因素，通过“均数–标准差”计算各影响因素重要性的满分频率与算术均数界值，以“均数+标准差”计算变异系数界值。删除影响因素需同时满足以下3个条件：（1）该因素满分频率<满分频率界值；（2）算术均数<算术均数界值；（3）变异系数>变异系数界值。若某因素有1～2个不满足条件，根据专家意见和课题组讨论后予以修改或删除；若3个条件都不满足，则保留该因素。基于此确定下一轮专家咨询问卷，进一步筛选与修订影响因素，直至专家观点趋于一致。
1.3.2　Fuzzy-DANP法
DANP法是对决策实验与评估实验室法（decision making trial and evaluation laboratory, DEMATEL）与网络层次分析法（analytic network process, ANP）的集成[28]，其有效结合了两者优势，不仅可以表征因素的重要性程度、刻画因素之间的相互影响程度，还从决策者和专家的角度弥补了ANP法在决策过程的复杂性[29]。为减少专家打分判断的主观性，研究中常结合模糊集理论将专家评分转换为三角模糊数[30–31]，形成Fuzzy-DANP法。鉴于该方法对专家数量并无明确要求，本研究邀请前期德尔菲专家咨询的16名专家再次组成专家组，以确保专家数量充足的同时避免人力资源浪费。具体步骤如下：（1）基于影响因素体系设计一级、二级影响因素两两间相互影响程度的专家打分问卷，采用Likert 5级评分法，从“无影响”到“影响非常大”分别赋值0～4分。得到的专家打分值，根据Chen C T等[32]设计的模糊尺度（表1）转化为三角模糊数，再通过CFCS法（converting fuzzy data into crisp scores）去模糊化[33]，建立直接影响矩阵B。（2）以行最大值为标准规范化处理矩阵B，按公式T=N/（I–N）得到综合影响矩阵T，其中I为n阶单位矩阵、N为规范化直接影响矩阵。（3）计算矩阵T的各行之和与各列之和，即为各因素的影响度Ri与被影响度Di；二者相加得到中心度Ri+Di，反映该因素在体系中的相对重要性；二者相减得到原因度Ri–Di，反映该因素对其他因素的影响情况，Ri–Di>0为原因因素，Ri–Di<0为结果因素。（4）以中心度为横轴、原因度为纵轴绘制因果关系图。（5）将综合影响矩阵T作为未加权超矩阵进行归一化处理，得到加权超矩阵S，矩阵S不断进行幂次方运算直到达到收敛状态，形成稳定的极限超矩阵L，即得到一级与二级影响因素的权重值。
1.3.3　秩和比法（rank sum ratio, RSR）[34]
用于三级影响因素的权重计算。将第2轮德尔菲咨询中每位专家对各三级影响因素的重要性评分排序编秩并计算秩和，以各三级影响因素的秩和与总秩和的比值作为其权重。
1.3.4　统计学方法
采用Microsoft Excel 2016和SPSS 26.0软件统计并分析德尔菲专家咨询问卷数据，采用Matlab R 2024a（The MathWorks, Inc.）软件进行以Fuzzy-DANP法为基础的矩阵构建及权重计算，以P<0.05为差异有统计学意义。
2　结　果
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2.1　影响因素体系确定
2.1.1　专家基本情况
17名参与咨询的专家年龄以41～50岁为主（47.06%），多为正高级职称（58.82%），以硕士研究生学历为主（70.59%），工作年限基本在15年以上。6人来自高校/科研机构、4人来自政府部门、3人来自疾病预防控制中心、3人来自卫生医疗机构、1人为社区工作人员。专家专业领域集中在卫生管理、疾病预防与控制、应急管理和流行病学，对研究内容的熟悉程度高，具有一定代表性。
2.1.2　专家积极性、权威程度及意见协调程度
两轮德尔菲专家咨询为同一批专家，均发放回收问卷17份，有效回收率均为100%，第1轮有8名专家提出相关意见，第2轮有5名专家提出相关意见。专家反馈积极性高。2轮专家的Ca值分别为0.95和0.90、Cs值分别为0.81和0.82，计算得Cr值分别为0.88和0.86，表明专家的权威程度较高。2轮专家咨询重要性评分的变异系数范围分别为0.005～0.226和0.000～0.225，因素波动较小，专家评估协调意见较好；2轮专家意见的Kendall协调系数W分别为0.181和0.193（均P<0.05），可以认为各专家意见具有一致性。
2.1.3　影响因素筛选结果（表2）
第1轮删除3项、合并1项、增加7项影响因素，修改8项影响因素名称表述；第2轮删除2项影响因素，其余均保留。最终确定包括5个一级影响因素、15个二级影响因素和47个三级影响因素的城市社区卫生应急多主体协同效应影响因素体系。
2.2　关键影响因素及权重确定（表3、图3）
按照Fuzzy-DANP方法步骤，得到一级与二级影响因素的影响度Ri、被影响度Di、中心度Ri+Di、原因度Ri-Di（表3）以及各级影响因素的权重（表3）。以“原因度=0”和“中心度=10.42（二级影响因素中心度的平均值）”为界，将二级影响因素划分为4个象限，绘制出二级影响因素的因果关系图（图3）。
3　讨　论
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本研究基于SFIC修正模型，采用Fuzzy-DANP方法，识别并量化了城市社区卫生应急多主体协同效应的影响因素及其相互作用关系，并确定了各因素的全局权重，可为政策制定者、社区管理者以及利益相关者在资源受限或协同失灵情境下提供精准干预的决策坐标。结果显示，一级影响因素的原因因素（Ri–Di>0）按其原因度大小依次为协同环境、协同监督、协同引擎，而结果因素（Ri–Di<0）依次为协同基础和协同行为。可见，协同基础和协同行为受到协同环境、协同监督和协同引擎的影响。其中，协同环境的原因度最高，表明在突发公共卫生事件防范与应对中，政治、经济和社会文化环境不可避免地对其他因素产生影响，进而影响协同效应的实现与发展；协同引擎的中心度最高，表明其在卫生应急中发挥的作用最为重要，这一结果与许金鹏等[35]的研究相符，即正确的领导、规范的制度以及思想共识是驱动协同过程稳定有序的关键。二级影响因素中的原因因素包括政策法规、经济发展、领导能力、社会文化、制度设计、组织结构、思想共识，容易对其他因素产生影响；而结果因素包括环境规划、资源配置、能力培育、多元行动、信息共享、资源整合、绩效评价、过程监控，易受其他因素影响，是推动协同效应提升的直接因素和重要媒介。其中，政策法规的原因度最高，说明相关政策法规的制定与完善更容易促进多主体协同效应的实现，与洪闯等[36]的研究发现一致，并且与我国“权责法定、预案先行”的公共卫生治理理念相契合；资源整合的被影响度最高，因此协同效应的有效实现最终取决于多主体之间是否能够建立起资源互补与共享的良好关系，与林晶晶[37]的研究结论一致；领导能力的中心度最高，表明其在推动各主体有效实现社区卫生应急目标中发挥最关键的作用，强有力的领导和明确的指挥对于确保快速、有序的应急响应至关重要[38]。
结合因果关系图，位于第一象限的C1、C2、C3和B3在原因度和中心度上均较高，即不仅对协同效应的影响最大，还在整个影响因素体系中发挥关键导向作用，为驱动因素（driving factor），在协同应对突发公共卫生事件中应予以重点和优先关注；位于第二象限的A1、A2和A3的中心度较低但原因度较高，均归属于协同环境，主要发挥辅助影响作用，为支援性因素（voluntariness）；位于第三象限的E1、E2和A4在原因度和中心度上均偏低，易受到第一、二象限中原因因素的影响，为独立性因素（independent），通常不需要采取直接的纠正措施；位于第四象限的B1、B2、D1、D2和D3中心度较高但原因度较低，在体系中占据着重要地位的同时又易受影响，为核心问题要素（core problem）也应予以重视。此外，领导能力、政策法规、制度设计、思想共识、组织结构、经济发展等二级因素权重较高，与表3中对二级因素中心度的排名趋势相近，主要涉及协同引擎、协同基础、协同环境层面，是提升多主体协同效应的关键发力点。其中，领导能力、制度设计、思想共识的权重与中心度均较高，为关键二级影响因素，同属于协同引擎层面，与一级因素权重结果的排序一致，进一步验证了研究结果的可靠性。在三级因素层面，政策法规支持力度、制度可操作性、信息准确性、资源获取及时性、信息共享及时度、人员队伍能力水平、风险识别能力、政策法规完善程度、卫生应急资金投入水平、权责利明确程度等权重较高，提示在城市社区卫生应急多主体协同过程中若能够优化和改善上述因素，可以显著提升协同联动的效率和效果。
城市社区卫生应急多主体协同效应影响因素繁多复杂。为了明晰未来城市社区卫生应急规划中应考虑的重点领域和因素，本研究以中心度、原因度和全局权重排序位次为依据，结合城市社区卫生应急多主体协同联动现状和协同效应影响因素的分析结果，提出以下优化策略：（1）因地制宜，强化协同政策支撑。政策法规是确保社会各方在公共卫生事件发生时高效、规范协作的最根本要素，缺乏健全有效的政策指导和法律支持会导致协同行动混乱，妨碍协同效应的实现[39]。目前，我国城市社区卫生应急相关政策存在流于形式、权责边界不清、法律保障机制不健全等问题[40]。因此，属地政府应结合社区实际，如特定的风俗习惯、地理环境和生活方式等，因地制宜制定相关政策。同时以明确的法律文件增强社区多主体协同行为的规范性与合法性，将卫生应急协同行动方案纳入城市社区应急预案体系，厘清各主体的职能和权责分工，明确界定社会组织和居民等社会力量的参与方式与渠道，落实协同合作流程，确保协同过程的透明与可实施性。（2）因势利导，发挥权威引领力。社区领导的统筹、干预、引导和管理能力，尤其是风险识别能力，是提升多主体协同效应的关键。但部分领导者受自身认知局限和传统行政思维的影响，易忽视社区的实际情况与健康需求，导致资源分配不均，多主体协同动力不足[41]。因此，有必要强化社区领导班子的公共卫生风险意识和底线思维，通过专业知识集体学习和专题培训提升其在社区应急管理中的胜任力[42]，同时组建常态化卫生应急专家智库，助力领导者提高指示与决策的科学性、专业性。此外，社区党政领导力的充分发挥还需依托于健全而灵活的组织结构。应加强民政与卫健体系的深度协同与融合，通过组织嵌入、组织整合等方式集聚应急力量，充分运用公共卫生委员会平台协同会商、研讨与推演研判，形成上下贯通、左右联动、对接通畅的常态化应急组织结构和扁平化的多元协同领导小组，从而显著提升决策的执行落实效率和协同性。（3）多元共育，筑牢思想共识基础。共同的意识理念和行动目标是协同行为的先决条件[5]，不同主体间能否维持长期稳定的协同关系，很大程度上取决于各方是否达成了协同目标共识。首先，社区工作人员、志愿者和社会工作者应定期接受应急知识与技能培训，与社区卫生机构组成专业化的“同盟军”，精准把握辖区居民需求，广泛开展卫生应急与健康知识科普宣传活动。其次，应健全社区事务多方评议的协商议事机制[43]，鼓励企业、社会组织和居民等利用新媒体平台或门户网站表达利益诉求，有关部门及时回应、联动化解矛盾。第三，社区应定期组织相关部门和单位联合开展卫生应急演练活动，通过提供消费代金券、荣誉奖状、职称晋升渠道等措施激励各方积极参与，并根据实际演练情况合理分配和调整目标任务，凝聚共治合力。（4）因需施策，优化数智支持。及时、准确的信息公开与共享，合理、有效的资源整合与调配，是多元主体形成应对合力的前提。目前，我国城市社区卫生应急信息化建设尚处于起步阶段，多数社区仍依赖电话、微信群等报送相关信息与对接沟通，存在信息滞后、信息误差、信息质量参差不齐以及“信息烟囱”等问题[44]。为此，一要发挥5G、大数据、人工智能等先进技术优势，以城市“一网统管”与“新基建”建设为契机，加快推进社区公共卫生应急管理综合智慧平台建设。推动社区各级各类组织机构在风险监测预警、应急指挥、资源调配、患者救治等工作中的跨部门、跨行业信息共享，建立数据高效融合、业务横纵协同的一体化运作机制。二要建立应急资源储备与发放机制。利用智慧平台整合社区内各部门单位的人、财、物等卫生应急资源的供需信息，为科学配给、快速调用以及精准投放提供决策支持。
本研究也存在一些局限性，所使用数据均来自于专家的经验赋值，结果可能存在一定的主观偏差。后续还需结合实际案例的客观数据开展实证研究，以进一步完善影响因素体系，并根据实践需求不断调整和优化协同联动模型，以便更深入地分析和探讨城市社区卫生应急多主体协同效应。

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doi: 10.11847/zgggws1146412
  • 接收时间：2024-11-03
  • 首发时间：2026-10-23
  • 出版时间：2026-04-10
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  • 收稿日期：2024-11-03
  • 修回日期：2025-09-02
  • 录用日期：2025-10-30
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    首都医科大学公共卫生学院，北京 100069

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2种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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