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Article(id=1320438177479905545, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, articleNumber=null, orderNo=null, doi=10.11847/zgggws1148431, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752249600000, receivedDateStr=2025-07-12, revisedDate=1760025600000, revisedDateStr=2025-10-10, acceptedDate=1772380800000, acceptedDateStr=2026-03-02, onlineDate=1792746594065, onlineDateStr=2026-10-23, pubDate=1775750400000, pubDateStr=2026-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792746594065, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792746594065, creator=13701087609, updateTime=1792746594065, 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=393, endPage=397, ext={EN=ArticleExt(id=1320438178310377739, articleId=1320438177479905545, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Construction of a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions: based on the Delphi method, 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 establish a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions, providing a tool for assessing the food safety risk monitoring capabilities of monitoring institutions.

Methods

Through literature review combined with practical monitoring work, a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions was preliminarily constructed across three dimensions: resource support, personnel capability, and monitoring technology. Twelve experts in relevant fields were selected for two rounds of Delphi expert consultation, and the analytic hierarchy process (AHP) was adopted to calculate the weights of indicators at each level.

Results

The two rounds of expert consultation showed the expert positive coefficients of 100.0% and 83.33%, the expert authority coefficients of 0.803 and 0.798, and the Kendall′s coefficients of concordance of 0.187 and 0.287 (indicating good concordance among expert opinions), respectively (all P < 0.001). Following expert consultation, a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions was established, comprising 3 first-level indicators, 9 second-level indicators, and 36 third-level indicators. In the two rounds of consultation, experts rated the importance of each indicator at (4.35 ± 0.93) and (4.42 ± 0.66) points, the representativeness at (4.19 ± 0.96) and (4.29 ± 0.66) points, and the feasibility at (4.28 ± 0.99) and (4.38 ± 0.74) points, respectively. The coefficients of variation (CV) for importance, representativeness, and feasibility scores in the first and second rounds were within the ranges of 0.08–0.45 and 0.02–0.22, 0.06–0.40 and 0.05–0.19, and 0.13–0.40 and 0.07–0.23, respectively. Within the constructed evaluation framework, first-level indicator weights ranged from 0.164 to 0.539, second-level indicator weights from 0.121 to 0.667, second-level indicator combination weights from 0.027 to 0.360, third-level indicator weights from 0.046 to 0.667, and third-level indicator combination weights from 0.002 to 0.129.

Conclusions

The comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions developed in this study demonstrates sound scientific validity and reliability. It can serve as a dependable, quantitative tool for assessing the overall capabilities of relevant monitoring technical institutions.

, authors=Jiyao AN1, Dajin YANG2, Xiufen GAO2, authorsList=Jiyao AN, Dajin YANG, Xiufen GAO, authorCompany=null, correspAuthors=Xiufen GAO, authorNote=null, correspAuthorsNote=
GAO Xiufen, E-mail:
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目的

构建国家食品安全风险监测技术机构综合能力评价指标体系，为开展监测机构食品安全风险监测能力评价提供工具。

方法

通过文献研究法结合实际监测工作从资源保障、人员能力和监测技术3个维度初步构建国家食品安全风险监测技术机构综合能力评价指标体系，遴选12名从事相关领域的专家进行2轮德尔菲专家咨询，并应用层次分析法计算各级指标的权重。

结果

2轮咨询的专家积极系数分别为100.0%和83.33%，专家权威系数分别为0.803和0.798，专家意见的协调系数分别为0.187和0.287（均P<0.001），专家意见的协调性较好；经过专家咨询后构建的国家食品安全风险监测技术机构综合能力评价指标体系包括一级指标3个、二级指标9个和三级指标36个，专家在2轮咨询中对各指标的重要性评分分别为（4.35±0.93）和（4.42±0.66）分，代表性评分分别为（4.19±0.96）和（4.29±0.66）分，可行性评分分别为（4.28±0.99）和（4.38±0.74）分，第1轮和第2轮重要性、代表性、可行性评分的CV分别为0.08～0.45和0.02～0.22、0.06～0.40和0.05～0.19、0.13～0.40和0.07～0.23；在构建的评价指标体系中，一级指标权重为0.164 ～ 0.539，二级指标权重为0.121～0.667，二级指标组合权重为0.027～0.360，三级指标权重为0.046～0.667，三级指标组合权重为0.002～0.129。

结论

本研究构建的国家食品安全风险监测技术机构综合能力评价指标体系具有良好的科学性和可靠性，可作为相关监测技术机构可靠、量化的综合能力评价工具。

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安纪瑶（2000—），硕士在读，研究方向：公共卫生

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高秀芬，E-mail：
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Comprehensive capability evaluation indicator system and indicator weights for national food safety risk monitoring technical institutions

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一级指标权重二级指标初始权重组合权重三级指标初始权重组合权重
A1　资源保障0.539B1　仪器设备0.6670.360C1　种类0.3600.129
C2　数量0.1240.045
C3　最近购买年份0.0670.024
C4　国外品牌含合资比例0.0460.017
C5　掌握各类仪器操作的人员数量0.2020.073
C6　机构仪器总价值0.2020.073
B2　财政投入0.3330.180C7　2021年财政0.1970.035
C8　2022年财政0.3110.056
C9　2023年财政0.4920.088
A2　人员能力0.297B3　人员数量0.1910.057C10　固定人员数量0.6670.038
C11　工作种类数量0.3330.019
B4　人员资质0.4190.124C12　专业类别0.2390.030
C13　第一学历0.1100.014
C14　最高学历0.1950.024
C15　奖项与任职0.0730.009
C16　职称0.1330.017
C17　工作时长0.0900.011
C18　专职比例0.1610.020
B5　团队综合产出0.1210.036C19　核心论文数0.1880.007
C20　出版专著数0.0870.003
C21　发明专利数0.0520.002
C22　团队奖项0.1300.005
C23　能力验证0.5440.020
B6　培训开展情况0.2690.080C24　培训种类0.4920.039
C25　培训数量0.1970.016
C26　参与人次0.3110.025
A3　监测技术0.164B7　监测开展情况0.2970.048C27　报告总数0.4130.020
C28　特色监测实施状态0.1870.009
C29　应急监测实施状态0.1080.005
C30　食品隐患挖掘状态0.2930.014
B8　技术能力0.1640.027C31　手册开展比例0.6670.018
C32　未开展原因0.3330.009
B9　质量管理0.5390.088C33　样品复验0.3890.034
C34　现场实调0.1970.017
C35　质控考核0.2760.024
C36　信息核查0.1390.012
), ArticleFig(id=1320438191258194227, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438177479905545, language=CN, label=表1, caption=

国家食品安全风险监测技术机构综合能力评价指标体系及其权重

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标权重二级指标初始权重组合权重三级指标初始权重组合权重
A1　资源保障0.539B1　仪器设备0.6670.360C1　种类0.3600.129
C2　数量0.1240.045
C3　最近购买年份0.0670.024
C4　国外品牌含合资比例0.0460.017
C5　掌握各类仪器操作的人员数量0.2020.073
C6　机构仪器总价值0.2020.073
B2　财政投入0.3330.180C7　2021年财政0.1970.035
C8　2022年财政0.3110.056
C9　2023年财政0.4920.088
A2　人员能力0.297B3　人员数量0.1910.057C10　固定人员数量0.6670.038
C11　工作种类数量0.3330.019
B4　人员资质0.4190.124C12　专业类别0.2390.030
C13　第一学历0.1100.014
C14　最高学历0.1950.024
C15　奖项与任职0.0730.009
C16　职称0.1330.017
C17　工作时长0.0900.011
C18　专职比例0.1610.020
B5　团队综合产出0.1210.036C19　核心论文数0.1880.007
C20　出版专著数0.0870.003
C21　发明专利数0.0520.002
C22　团队奖项0.1300.005
C23　能力验证0.5440.020
B6　培训开展情况0.2690.080C24　培训种类0.4920.039
C25　培训数量0.1970.016
C26　参与人次0.3110.025
A3　监测技术0.164B7　监测开展情况0.2970.048C27　报告总数0.4130.020
C28　特色监测实施状态0.1870.009
C29　应急监测实施状态0.1080.005
C30　食品隐患挖掘状态0.2930.014
B8　技术能力0.1640.027C31　手册开展比例0.6670.018
C32　未开展原因0.3330.009
B9　质量管理0.5390.088C33　样品复验0.3890.034
C34　现场实调0.1970.017
C35　质控考核0.2760.024
C36　信息核查0.1390.012
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国家食品安全风险监测技术机构综合能力评价指标体系构建：基于德尔菲专家咨询法
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安纪瑶 1 , 杨大进 2 , 高秀芬 2, *, *
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): 393-397
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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) : 393 -397
国家食品安全风险监测技术机构综合能力评价指标体系构建：基于德尔菲专家咨询法
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安纪瑶1, 杨大进2, 高秀芬2, *, *
作者信息
  • 1中国疾病预防控制中心营养与健康所，北京 100032
  • 2国家食品安全风险评估中心，北京 100022
通讯作者:
高秀芬，E-mail：
作者简介:

安纪瑶（2000—），硕士在读，研究方向：公共卫生

Construction of a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions: based on the Delphi method
Jiyao AN1, Dajin YANG2, Xiufen GAO2
Affiliations
  • 1National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100032, China
  • 2China National Center for Food Safety Risk Assessment, Beijing 100022, China
    • Corresponding Author:
    GAO Xiufen, E-mail:
出版时间: 2026-04-10 doi: 10.11847/zgggws1148431
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摘要
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目的

构建国家食品安全风险监测技术机构综合能力评价指标体系，为开展监测机构食品安全风险监测能力评价提供工具。

方法

通过文献研究法结合实际监测工作从资源保障、人员能力和监测技术3个维度初步构建国家食品安全风险监测技术机构综合能力评价指标体系，遴选12名从事相关领域的专家进行2轮德尔菲专家咨询，并应用层次分析法计算各级指标的权重。

结果

2轮咨询的专家积极系数分别为100.0%和83.33%，专家权威系数分别为0.803和0.798，专家意见的协调系数分别为0.187和0.287（均P<0.001），专家意见的协调性较好；经过专家咨询后构建的国家食品安全风险监测技术机构综合能力评价指标体系包括一级指标3个、二级指标9个和三级指标36个，专家在2轮咨询中对各指标的重要性评分分别为（4.35±0.93）和（4.42±0.66）分，代表性评分分别为（4.19±0.96）和（4.29±0.66）分，可行性评分分别为（4.28±0.99）和（4.38±0.74）分，第1轮和第2轮重要性、代表性、可行性评分的CV分别为0.08～0.45和0.02～0.22、0.06～0.40和0.05～0.19、0.13～0.40和0.07～0.23；在构建的评价指标体系中，一级指标权重为0.164 ～ 0.539，二级指标权重为0.121～0.667，二级指标组合权重为0.027～0.360，三级指标权重为0.046～0.667，三级指标组合权重为0.002～0.129。

结论

本研究构建的国家食品安全风险监测技术机构综合能力评价指标体系具有良好的科学性和可靠性，可作为相关监测技术机构可靠、量化的综合能力评价工具。

食品安全  /  风险  /  监测技术机构  /  综合能力  /  评价指标体系  /  构建
Objective

To establish a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions, providing a tool for assessing the food safety risk monitoring capabilities of monitoring institutions.

Methods

Through literature review combined with practical monitoring work, a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions was preliminarily constructed across three dimensions: resource support, personnel capability, and monitoring technology. Twelve experts in relevant fields were selected for two rounds of Delphi expert consultation, and the analytic hierarchy process (AHP) was adopted to calculate the weights of indicators at each level.

Results

The two rounds of expert consultation showed the expert positive coefficients of 100.0% and 83.33%, the expert authority coefficients of 0.803 and 0.798, and the Kendall′s coefficients of concordance of 0.187 and 0.287 (indicating good concordance among expert opinions), respectively (all P < 0.001). Following expert consultation, a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions was established, comprising 3 first-level indicators, 9 second-level indicators, and 36 third-level indicators. In the two rounds of consultation, experts rated the importance of each indicator at (4.35 ± 0.93) and (4.42 ± 0.66) points, the representativeness at (4.19 ± 0.96) and (4.29 ± 0.66) points, and the feasibility at (4.28 ± 0.99) and (4.38 ± 0.74) points, respectively. The coefficients of variation (CV) for importance, representativeness, and feasibility scores in the first and second rounds were within the ranges of 0.08–0.45 and 0.02–0.22, 0.06–0.40 and 0.05–0.19, and 0.13–0.40 and 0.07–0.23, respectively. Within the constructed evaluation framework, first-level indicator weights ranged from 0.164 to 0.539, second-level indicator weights from 0.121 to 0.667, second-level indicator combination weights from 0.027 to 0.360, third-level indicator weights from 0.046 to 0.667, and third-level indicator combination weights from 0.002 to 0.129.

Conclusions

The comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions developed in this study demonstrates sound scientific validity and reliability. It can serve as a dependable, quantitative tool for assessing the overall capabilities of relevant monitoring technical institutions.

food safety  /  risk  /  monitoring technical institutions  /  comprehensive capability  /  evaluation indicator system  /  construction
安纪瑶, 杨大进, 高秀芬. 国家食品安全风险监测技术机构综合能力评价指标体系构建：基于德尔菲专家咨询法. 中国公共卫生, 2026 , 42 (4) : 393 -397 . DOI: 10.11847/zgggws1148431
Jiyao AN, Dajin YANG, Xiufen GAO. Construction of a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions: based on the Delphi method[J]. Chinese Journal of Public Health, 2026 , 42 (4) : 393 -397 . DOI: 10.11847/zgggws1148431
正文
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能力评价作为系统化评估手段，能够通过多维度量化分析客观识别个体或组织在能力建设中的优势与不足，为人才与资源的合理配置提供依据，目前已在企业管理、教育改革和人员选拔等多个领域得到广泛应用[1–5]。食品安全风险监测是食品安全治理体系中的关键环节，其通过系统、科学的监测及时发现潜在隐患，为监管决策提供支持，对防范食品安全事件发生、提升公共健康保障水平具有重要意义[6]。而食品安全风险监测技术机构作为风险监测工作的实施主体，其能力水平将直接关系到监测结果的可靠性与有效性。近年来，部分地区的食品安全风险监测技术机构已开展了一系列能力评估实践，如通过定期检查、实验室间比对和能力验证等方式对机构的检测设备、人员资质和流程规范等方面进行考核[7]，但评价方式较为单一，多以检查和考核为主，不能反映食品安全风险监测技术机构的综合能力，且评价标准不一，导致评价结果无法量化而缺乏可比性。为构建国家食品安全风险监测技术机构综合能力评价指标体系，为开展监测机构食品安全风险监测能力评价提供工具，本研究通过文献研究法结合实际监测工作从资源保障、人员能力和监测技术3个维度初步构建国家食品安全风险监测技术机构综合能力评价指标体系，遴选12名从事相关领域的专家进行了2轮德尔菲专家咨询，并应用层次分析法计算各级指标的权重。结果报告如下。
1　资料与方法
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1.1　确定指标体系框架
基于资源基础观“资源—能力—绩效”框架，资源是价值创造的基础，能力体现资源的整合与转化，绩效反映价值实现的成果[8]。本研究结合食品风险监测实际，通过文献检索筛选与机构能力评价相关文献[9–13]提取重要的评价指标，并根据实际监测工作经验对指标进行调整，初步形成包括资源保障、人员能力、监测技术3个一级指标及其8个二级指标和37个三级指标的国家食品安全风险监测技术机构综合能力评价指标体系初始框架。由此形成调查问卷，进行德尔菲专家咨询。
1.2　德尔菲专家咨询法
对遴选出的12名北京市、广东省、湖南省、四川省、西藏自治区等省（自治区、直辖市）疾病预防控制中心的专家进行咨询，专家均为长期从事食品安全风险监测技术和管理等工作且具备本科及以上学历的副高级及以上职称人员，并知情同意且具有良好的参与积极性。本研究通过电子邮件邀请专家在线填写指标体系调查表，对初步构建的国家食品安全风险监测技术机构综合能力评价指标体系提出修改建议，并采用Likert 5级量表（1表示态度最弱，3表示中立，5表示态度最强）[14]对指标的重要性、代表性和可行性进行量化评分。收到专家反馈后，根据各指标在重要性、代表性和可行性方面的评分结果，以重要性评分>3.50且变异系数（coefficient of variation，CV）<0.25作为筛选标准[15]，同时结合专家所提出的修改意见，并综合考量指标的实际意义与相互替代关系，对指标体系进行多轮调整与优化，直至所有指标均满足上述标准且专家无新增意见。经过2轮有效的专家咨询后，最后根据专家意见确定国家食品安全风险监测技术机构综合能力评价指标体系。
1.3　统计分析
采用Excel 2016录入数据建立数据库，应用R 4.5.0进行统计分析。专家咨询结果的可靠性需从积极性、权威性和协调度3个维度综合评估。积极性通过咨询结果回收率体现，回收率>70%为良好，>80%表明专家积极性非常高[16]。权威性由权威系数（Cr）衡量，其计算公式为Cr=（Ca+Cs）/2，其中判断系数（Ca）是专家依据理论分析、实践经验、国内外同行了解和个人直觉4个维度对自身判断影响程度（大、中、小）的评分总和，熟悉系数（Cs）则依据专家对问题的熟悉程度（非常熟悉=0.9、熟悉=0.7、较熟悉=0.5、一般熟悉=0.3、较不熟悉=0.1、很不熟悉=0）评定，Cr≥0.70表明权威性良好[17–18]。采用肯德尔协调系数（Kendall′s W）评价专家意见的协调程度。W值的范围为0～1，W值越大表明专家意见的一致性越高；采用χ2检验进行显著性分析，若P值<0.05，可认为专家意见具有显著协调性[19]。专家意见的集中程度以各指标重要性评分的算数均数及CV表示，均数越大，该指标的重要性越高；变异系数越小，则专家评价意见越集中[20]。采用层次分析法对各级指标权重进行定量计算，基于专家重要性评分的均值差（Z）确定Saaty标度，构建判断矩阵[21]；使用公式CI=（λmax–n）/（n–1）计算一致性指数（consistency index, CI），其中λmax为判断矩阵的最大特征根，n为指标数；通过查表获取相应阶数的平均随机一致性指标（random consistency index, RI）[22]，并进一步计算一致性比率（consistent ratio, CR）CR=CI/RI，若CR<0.1，则认为该判断矩阵通过一致性检验[16]。在确定各层级指标的权重后，采用乘积法计算同级指标对上一级指标的组合权重。
2　结　果
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2.1　专家基本情况
本研究遴选的12名专家中，男性8人（66.67%），女性4人（33.33%）；年龄30～39岁2人（16.67%），40～49岁7人（58.33%），50～59岁3人（25.00%）；文化程度本科2人（16.67%），硕士4人（33.33%），博士6人（50.00%）；副高级职称4人（33.33%），正高级职称8人（66.67%）；专业领域为从事食品安全技术管理相关工作9人（0.75%），从事实验室检测相关工作2人（16.67%），从事食品安全政策研究与规划相关工作1人（8.33%）；工作年限<10年1人（8.33%），10～19年5人（41.67%），20～29年5人（41.67%），≥30年1人（8.33%）。
2.2　专家积极系数
第1轮专家咨询发出问卷12份，回收问卷12份，专家积极系数为100%；12名专家中有9人提出69条专业建议，提出建议率为75.00%。第2轮专家咨询发出问卷12份，回收10份，专家积极系数为83.33%；10名专家中有6人提出20条专业建议，提出建议率为60.00%。说明专家的积极性较高。
2.3　专家权威系数
第1轮专家判断系数Ca为0.922，熟悉程度系数Cs为0.683，权威系数Cr为0.803；第2轮专家判断系数Ca为0.916，熟悉程度系数Cs为0.680，权威系数Cr为0.798。
2.4　专家协调系数
2轮咨询的专家意见协调系数分别为0.187和0.287，差异均有统计学意义（均P<0.001），表明专家意见的协调程度较好。
2.5　专家意见的集中程度
第1轮指标的重要性评分为（4.35±0.93）分，代表性评分为（4.19±0.96）分，可行性评分为（4.28±0.99）分，重要性、代表性和可行性评分的CV分别为0.08～0.45、0.06～0.40和0.13～0.40；第2轮指标的重要性评分为（4.42±0.66）分，代表性评分为（4.29±0.66）分，可行性评分为（4.38±0.74）分，重要性、代表性和可行性评分的CV分别为0.02～0.22、0.05～0.19和0.07～0.23。经过2轮专家咨询，指标重要性、代表性和可行性评分均值有所提高，专家分歧更小，意见更为统一。
2.6　国家食品安全风险监测技术机构综合能力评价指标体系及其权重（表1）
第1轮专家咨询后，在“人员能力”方面新增了“人员资质”“能力提升”“人员数量”3项二级指标和“人员专业类别”1项三级指标，将二级指标“团队能力”修改为“团队综合产出”，三级指标“人员进入数”与“人员调出数”合并为“固定人员数量”，“承担工作种类”调整为“工作种类数量”，“日常报告数量”修改为“报告总数”；在“监测技术”方面删除了“创新提升”1项二级指标和“方法创新”“方法调整”2个三级指标，并调整三级指标“日常报告数量”为“报告总数”，“特色监测数量”为“特色监测实施状态”，“应急监测数量”为“应急监测实施状态”，“食品隐患数量”为“食品隐患挖掘状态”。第2轮专家咨询后，将“人员能力”维度中的二级指标“能力提升”修改为“培训开展情况”，“监测技术”维度中的二级指标“监测报告”修改为“监测开展情况”，将“人员能力”中专业类别的划分由“检验类、化学类、预防类、临床类与其他”改为“微生物检验类、分析化学类（含药学）、食品检验检疫类、预防医学类、临床检验类与其他”，明确“固定人员数量”为稳定在职人员，不包含短期实习进修人员。经2轮调整与优化后，最后确立了由3个一级指标、9个二级指标和36个三级指标组成的国家食品安全风险监测技术机构综合能力评价指标体系；其中，一级指标权重为0.164 ～ 0.539，二级指标初始权重为0.121～0.667，二级指标组合权重为0.027～0.360，三级指标初始权重为0.046～0.667，三级指标组合权重为0.002～0.129。
3　讨　论
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本研究针对当前中国食品安全风险监测能力评价中存在的研究区域有限、指标单一、方法不同和结果难以量化比较等问题，结合监测工作的实际需求，在既往研究[9–13]基础上构建了一套技术机构综合监测能力评价指标体系。该体系依托“资源—能力—绩效”理论框架，涵盖资源保障、人员能力和监测技术3个一级指标及其9个二级指标和36个三级指标，实现了从单一要素评价向多维度、系统性综合评价的转变，并可对技术机构综合监测能力进行量化评估与横向比较。
在方法上，本研究综合运用德尔菲专家咨询法和层次分析法，确保指标筛选与权重确定的科学性和客观性。德尔菲法的2轮专家咨询中，12名专家人数满足Mitchell V W[23]提出的专家咨询人数不少于8～10人的标准，专家均来自相关领域且均具备高级职称与丰富实践经验，2轮专家积极系数均>80%，权威系数均>0.70，表明专家参与积极性高、权威性强[24]。从意见协调程度看，第2轮专家咨询CV均较第1轮专家咨询有所下降，专家意见的协调系数从第1轮专家咨询的0.187升至第2轮专家咨询的0.287，反映出多轮反馈有效促进了共识形成。层次分析法则通过构建判断矩阵降低主观偏差，所有指标CR值均<0.1，表明权重赋值合理、结构一致，增强了评价结果的可靠性与可比性。
权重分析结果显示，资源保障（0.539）对监测能力的影响最大，其次为人员能力（0.297）和监测技术（0.164），符合资源基础理论中基础资源决定机构优势的观点[25]。在资源保障维度中，二级指标中“仪器设备”组合权重最高（0.360），其下属三级指标中“种类”组合权重居首（0.129），说明设备功能完备性比数量或价值更具代表性。在人员能力维度中，二级指标中“人员资质”组合权重最高（0.124），与张瑞瑞[26]研究中“人员资质是保障实验室人员能力水平的重要手段”的结论相一致，说明人员专业素质和技术水平是提升食品安全风险监测能力的关键因素；三级指标中“培训种类”组合权重最高（0.039），提示应注重培训内容与实际需求的契合，这说明在人员管理中，应聚焦岗位实际，科学设计培训方案，以提升培训的针对性与实效性[27]。在监测技术维度，二级指标中“质量管理”（0.088）和三级指标中“样品复验”（0.034）的组合权重最高，凸显全流程质量控制和复核机制在监测能力中的核心作用[28]。
本研究通过构建可量化、可比较的综合评价体系，弥补了已有研究的不足，为监测机构能力建设提供了理论依据与实践工具。但在构建食品安全风险监测综合能力评价指标体系过程中仍存在一定局限性，如专家在遴选与指标评定环节可能受主观判断与经验影响而对体系的普适性和准确性造成偏差，因此，在后续研究中可通过扩大专家样本范围、引入多源数据与客观分析方法，并在不同地区和机构中开展实证检验来不断优化指标体系，以提升食品安全风险监测能力评价的科学性与可操作性；未来还可进一步拓展该体系的应用范围，并在动态评估与政策干预效果验证方面进行更深入的探索。

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doi: 10.11847/zgggws1148431
  • 接收时间：2025-07-12
  • 首发时间：2026-10-23
  • 出版时间：2026-04-10
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  • 收稿日期：2025-07-12
  • 修回日期：2025-10-10
  • 录用日期：2026-03-02
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    1中国疾病预防控制中心营养与健康所，北京 100032
    2国家食品安全风险评估中心，北京 100022

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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
栓菌属 Trametes 5 2.39
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