Article(id=1324050827165434414, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050669933572771, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202511469, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1764086400000, receivedDateStr=2025-11-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793607916871, onlineDateStr=2026-11-02, pubDate=1778342400000, pubDateStr=2026-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793607916871, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793607916871, creator=13701087609, updateTime=1793607916871, updator=13701087609, issue=Issue{id=1324050669933572771, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='9', pageStart='1537', pageEnd='1728', issueExtLink='null', onlineDate='null', pubDate='1778342400000', pubDateStr='2026-05-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793607879384, creator='13701087609', updateTime=1793670100562, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324311644528050975, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050669933572771, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324311644528050976, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050669933572771, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1644, endPage=1650, ext={EN=ArticleExt(id=1324050829635879472, articleId=1324050827165434414, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Configurational analysis of mental health resource allocation and efficiency optimization pathways in Province S: an fsQCA study, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the current status and efficiency optimization pathways of mental health resource allocation in Province S and to provide empirical evidence for optimizing provincial resource allocation and improving resource utilization efficiency.

Methods

Based on mental health resource data from 16 prefecture-level cities in Province S in 2024, this study employed Data Envelopment Analysis (DEA) to evaluate the efficiency of resource allocation and used Fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify configurational conditions associated with different efficiency levels.

Results

Mental health resources in Province S have exhibited rapid growth but remain unevenly distributed overall. Among the 16 prefecture-level cities in Province S, only four cities, namely JN, DY, WF, and WH achieved strong DEA efficiency, with an average overall efficiency score of 0.829. Three high-efficiency configurational pathways were identified, namely the economic support-oriented, service-driven, and resource integration-oriented types, with an overall consistency of 0.808. In addition, one low-efficiency pathway, characterized by input imbalance, was identified, with an overall consistency of 0.907.

Conclusion

The overall efficiency of mental health resource allocation in Province S still has considerable room for improvement. Prefecture-level cities can optimize the layout and supply patterns of mental health resources according to their specific configurational characteristics and local conditions, and explore context-specific pathways to enhance the overall performance of the provincial mental health service system.

, authors=Ling-ting WANG1, 4, Zhen-lei TAN2, Jia-chen WANG2, Tian-zheng LIU2, Pei-long LI2, Chao-yang TANG2, Xue XIAO3, Jing-jie SUN2, Yang DU4, Li-yuan LIU2, authorsList=Ling-ting WANG, Zhen-lei TAN, Jia-chen WANG, Tian-zheng LIU, Pei-long LI, Chao-yang TANG, Xue XIAO, Jing-jie SUN, Yang DU, Li-yuan LIU, authorCompany=null, correspAuthors=Yang DU, Li-yuan LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324050833800823353, articleId=1324050827165434414, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于fsQCA组态视角的S省精神卫生资源配置现状与效率优化路径研究, columnId=1228016568014139551, journalTitle=现代预防医学, columnName=卫生政策与管理, runingTitle=null, highlight=null, articleAbstract=
目的

分析S省精神卫生资源配置现状及效率优化路径,为优化全省精神卫生资源配置,提升资源利用效率提供实证依据。

方法

基于2024年S省16个地市的精神卫生资源数据,运用数据包络分析(data envelopment analysis,DEA)测算资源配置效率,并通过模糊集定性比较分析法得出不同效率水平的条件组态。

结果

S省精神卫生资源总体呈现“增长快而分布不均”的特征。16个地市中,仅JN、DY、WF和WH 4市达到DEA强有效,综合效率均值为0.829。识别出三条高效率配置路径,分别为经济支撑型、服务驱动型和资源整合型,其总体一致性为0.808;同时识别出一条低效率配置路径,即投入失衡型,其总体一致性为0.907。

结论

S省精神卫生资源配置效率整体仍有较大提升空间。各地市可根据不同组态特征,结合本地实际优化资源布局与供给模式,因地制宜探索适配路径,从而提升全省精神卫生服务体系整体效能。

, authors=王灵婷1, 4, 谭振磊2, 王家琛2, 刘天征2, 李培龙2, 汤朝阳2, 肖雪3, 孙经杰2, 杜杨4, 刘丽媛2, authorsList=王灵婷, 谭振磊, 王家琛, 刘天征, 李培龙, 汤朝阳, 肖雪, 孙经杰, 杜杨, 刘丽媛, authorCompany=null, correspAuthors=杜杨, 刘丽媛, authorNote=

王灵婷(2001—),女,硕士在读,研究方向:卫生政策与卫生事业管理

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杜杨,E-mail:;
刘丽媛,E-mail:;
杜杨与刘丽媛为共同通信作者
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figs=[ArticleFig(id=1324050845075112577, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Figure 1, caption=Changes in the number of mental health institutions,2020—2024, figureFileSmall=lzPvyCu5SEUm5nnx/vS2Vg==, figureFileBig=uSTugSdQHGOAFt5Vq5t48Q==, tableContent=null), ArticleFig(id=1324050845146415746, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=CN, label=图1, caption=2020—2024年精神卫生医疗机构数变化情况, figureFileSmall=lzPvyCu5SEUm5nnx/vS2Vg==, figureFileBig=uSTugSdQHGOAFt5Vq5t48Q==, tableContent=null), ArticleFig(id=1324050848178897539, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Figure 2, caption=Changes in the number of beds in mental health institutions, 2020—2024, figureFileSmall=zdSoDpiDKsSFWjxnGd1C3A==, figureFileBig=l+dCzMXdDGq8BlZ1ca+TzA==, tableContent=null), ArticleFig(id=1324050849269416580, 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journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Figure 4, caption=Changes in the average number of discharged patients in mental health institutions, 2020—2024, figureFileSmall=5auQ8q//8xXfw903rkAsEA==, figureFileBig=qe6sKKt3ybJ70ZY58sM77w==, tableContent=null), ArticleFig(id=1324050853119787657, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=CN, label=图4, caption=2020—2024年精神卫生医疗机构平均出院人数变化情况, figureFileSmall=5auQ8q//8xXfw903rkAsEA==, figureFileBig=qe6sKKt3ybJ70ZY58sM77w==, tableContent=null), ArticleFig(id=1324050853639881355, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Table 1, caption=

Basic description of the current status of mental health resources in Province S in 2024

, figureFileSmall=null, figureFileBig=null, tableContent=
主要资源指标机构层级机构级别
省级地市级区县级三级二级一级及未评级
精神卫生机构数(家)5209523219440
平均实有床位数(张)80.00(24.00,1 976.00)175.00(100.00,299.00)170.00(91.50,348.25)502.50(106.00,962.50)160.00(99.00,299.00)170.00(96.75,302.75)
平均床位使用率(%)85.21(54.86,96.51)90.96(74.21,100.00)85.32(65.38,97.06)98.85(92.13,109.25)88.10(65.35,98.32)89.52(78.12,98.53)
平均住院日(天)33.68(18.45,83.61)53.70(30.50,68.61)54.10(29.55,71.32)41.34(17.73,57.08)55.11(30.74,68.78)56.33(34.58,71.66)
平均门急诊人次(人次/年)53 438.00(16 144.00,381 374.50)1 765.00(630.50,9 903.50)14 524.00(5 064.75,43 984.50)48 607.00(3 640.00,173 819.50)1 733.00(626.00,9 797.75)9 539.00(1 371.25,43 865.25)
平均出院人数(人次/年)417.00(220.00,18 594.50)811.00(368.00,1 586.50)1 069.00(394.75,1 886.25)2 772.50(656.00,10 157.00)766.00(360.75,1 384.50)1 223.50(356.00,2 309.25)
平均执业(助理)医师8.00(6.50,252.50)11.00(7.00,21.00)15.50(5.00,23.50)39.50(7.50,163.00)10.00(7.00,18.25)14.50(4.25,21.75)
平均注册护士19.00(7.00,516.00)51.00(30.00,88.50)34.00(15.00,76.75)198.00(14.75,352.00)47.00(27.00,81.00)48.00(18.25,64.25)
), ArticleFig(id=1324050853954454156, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=CN, label=表1, caption=

2024年S省精神卫生资源现状基本描述

, figureFileSmall=null, figureFileBig=null, tableContent=
主要资源指标机构层级机构级别
省级地市级区县级三级二级一级及未评级
精神卫生机构数(家)5209523219440
平均实有床位数(张)80.00(24.00,1 976.00)175.00(100.00,299.00)170.00(91.50,348.25)502.50(106.00,962.50)160.00(99.00,299.00)170.00(96.75,302.75)
平均床位使用率(%)85.21(54.86,96.51)90.96(74.21,100.00)85.32(65.38,97.06)98.85(92.13,109.25)88.10(65.35,98.32)89.52(78.12,98.53)
平均住院日(天)33.68(18.45,83.61)53.70(30.50,68.61)54.10(29.55,71.32)41.34(17.73,57.08)55.11(30.74,68.78)56.33(34.58,71.66)
平均门急诊人次(人次/年)53 438.00(16 144.00,381 374.50)1 765.00(630.50,9 903.50)14 524.00(5 064.75,43 984.50)48 607.00(3 640.00,173 819.50)1 733.00(626.00,9 797.75)9 539.00(1 371.25,43 865.25)
平均出院人数(人次/年)417.00(220.00,18 594.50)811.00(368.00,1 586.50)1 069.00(394.75,1 886.25)2 772.50(656.00,10 157.00)766.00(360.75,1 384.50)1 223.50(356.00,2 309.25)
平均执业(助理)医师8.00(6.50,252.50)11.00(7.00,21.00)15.50(5.00,23.50)39.50(7.50,163.00)10.00(7.00,18.25)14.50(4.25,21.75)
平均注册护士19.00(7.00,516.00)51.00(30.00,88.50)34.00(15.00,76.75)198.00(14.75,352.00)47.00(27.00,81.00)48.00(18.25,64.25)
), ArticleFig(id=1324050855984497293, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Table 2, caption=

DEA results on mental health resource allocation efficiency across cities in Province S

, figureFileSmall=null, figureFileBig=null, tableContent=
地市综合效率(TE)纯技术效率(PTE)规模效率(SE)规模报酬相对有效性
JN市1.0001.0001.000不变DEA强有效
QD市0.6381.0000.638递减DEA弱有效
ZB市0.7410.8340.889递增非DEA有效
ZZ市0.9110.9290.980递减非DEA有效
DY市1.0001.0001.000不变DEA强有效
YT市0.6580.6770.971递减非DEA有效
WF市1.0001.0001.000不变DEA强有效
JNi市0.9790.9840.995递减非DEA有效
TA市0.6920.7110.973递减非DEA有效
WH市1.0001.0001.000不变DEA强有效
RZ市0.9460.9570.988递增非DEA有效
LY市0.6820.9900.689递减非DEA有效
DZ市0.6880.6891.000不变非DEA有效
LC市0.8520.8690.980递减非DEA有效
BZ市0.5280.5480.964递减非DEA有效
HZ市0.9471.0000.947递减DEA弱有效
均值0.8290.8870.938
), ArticleFig(id=1324050856403927694, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=CN, label=表2, caption=

S省各市精神卫生资源配置效率DEA结果

, figureFileSmall=null, figureFileBig=null, tableContent=
地市综合效率(TE)纯技术效率(PTE)规模效率(SE)规模报酬相对有效性
JN市1.0001.0001.000不变DEA强有效
QD市0.6381.0000.638递减DEA弱有效
ZB市0.7410.8340.889递增非DEA有效
ZZ市0.9110.9290.980递减非DEA有效
DY市1.0001.0001.000不变DEA强有效
YT市0.6580.6770.971递减非DEA有效
WF市1.0001.0001.000不变DEA强有效
JNi市0.9790.9840.995递减非DEA有效
TA市0.6920.7110.973递减非DEA有效
WH市1.0001.0001.000不变DEA强有效
RZ市0.9460.9570.988递增非DEA有效
LY市0.6820.9900.689递减非DEA有效
DZ市0.6880.6891.000不变非DEA有效
LC市0.8520.8690.980递减非DEA有效
BZ市0.5280.5480.964递减非DEA有效
HZ市0.9471.0000.947递减DEA弱有效
均值0.8290.8870.938
), ArticleFig(id=1324050856504590991, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Table 3, caption=

Calibration anchors for each variable

, figureFileSmall=null, figureFileBig=null, tableContent=
变量指标描述目标集合校准锚点
完全隶属(95%)交叉点(50%)完全不隶属(5%)
Y精神卫生资源配置效率结果变量1.0000.8820.611
X1人均GDP(万元/人)社会经济16.8858.7605.595
X2人均一般公共预算支出(万元/人)政府支持1.7251.1300.883
X3每千人口床位数(张)设施基础0.9830.6200.388
X4每千人口卫生技术人员数人力资源0.5630.2450.165
X5每千人口门急诊量(人次)服务利用116.53056.71026.283
), ArticleFig(id=1324050856827552400, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=CN, label=表3, caption=

各变量校准锚点

, figureFileSmall=null, figureFileBig=null, tableContent=
变量指标描述目标集合校准锚点
完全隶属(95%)交叉点(50%)完全不隶属(5%)
Y精神卫生资源配置效率结果变量1.0000.8820.611
X1人均GDP(万元/人)社会经济16.8858.7605.595
X2人均一般公共预算支出(万元/人)政府支持1.7251.1300.883
X3每千人口床位数(张)设施基础0.9830.6200.388
X4每千人口卫生技术人员数人力资源0.5630.2450.165
X5每千人口门急诊量(人次)服务利用116.53056.71026.283
), ArticleFig(id=1324050857288925841, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Table 4, caption=

Results of the necessity analysis for individual antecedent conditions

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件高效配置低效配置
一致性覆盖度一致性覆盖度
人均GDP高0.5660.6170.5170.543
人均GDP低0.5810.5550.6360.586
人均一般公共预算支出高0.5150.5520.5880.609
人均一般公共预算支出低0.6350.6150.5670.530
每千人口床位数多0.6410.6160.6010.557
每千人口床位数少0.5390.5830.5870.612
每千人口卫生技术人员数多0.5170.6220.5140.597
每千人口卫生技术人员数少0.6650.5860.6740.574
每千人口门急诊量多0.7090.7200.4810.472
每千人口门急诊量少0.4800.4890.7150.703
), ArticleFig(id=1324050857649635986, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=CN, label=表4, caption=

单个前因条件的必要性分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件高效配置低效配置
一致性覆盖度一致性覆盖度
人均GDP高0.5660.6170.5170.543
人均GDP低0.5810.5550.6360.586
人均一般公共预算支出高0.5150.5520.5880.609
人均一般公共预算支出低0.6350.6150.5670.530
每千人口床位数多0.6410.6160.6010.557
每千人口床位数少0.5390.5830.5870.612
每千人口卫生技术人员数多0.5170.6220.5140.597
每千人口卫生技术人员数少0.6650.5860.6740.574
每千人口门急诊量多0.7090.7200.4810.472
每千人口门急诊量少0.4800.4890.7150.703
), ArticleFig(id=1324050858102620819, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=EN, label=Table 5, caption=

Configurations of mental health resource allocation at different efficiency levels

, figureFileSmall=null, figureFileBig=null, tableContent=
条件变量高效配置组态低效配置组态
H1H2H3L
人均GDP●□○■
人均一般公共预算支出■■●●
每千人口床位数○○■□
每千人口卫生技术人员数□○●□
每千人口门急诊量□●○□
一致性0.8560.8380.8090.907
原始覆盖度0.2050.3610.2140.259
唯一覆盖度0.0600.2090.0930.259
总体一致性0.8080.907
总体覆盖度0.5190.259
), ArticleFig(id=1324050858488496788, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050827165434414, language=CN, label=表5, caption=

精神卫生资源不同效率配置组态

, figureFileSmall=null, figureFileBig=null, tableContent=
条件变量高效配置组态低效配置组态
H1H2H3L
人均GDP●□○■
人均一般公共预算支出■■●●
每千人口床位数○○■□
每千人口卫生技术人员数□○●□
每千人口门急诊量□●○□
一致性0.8560.8380.8090.907
原始覆盖度0.2050.3610.2140.259
唯一覆盖度0.0600.2090.0930.259
总体一致性0.8080.907
总体覆盖度0.5190.259
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基于fsQCA组态视角的S省精神卫生资源配置现状与效率优化路径研究
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王灵婷 1, 4 , 谭振磊 2 , 王家琛 2 , 刘天征 2 , 李培龙 2 , 汤朝阳 2 , 肖雪 3 , 孙经杰 2 , 杜杨 4 , 刘丽媛 2
现代预防医学 | 卫生政策与管理 2026,53(9): 1644-1650
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现代预防医学 |卫生政策与管理 2026 , 53 (9) : 1644 -1650
基于fsQCA组态视角的S省精神卫生资源配置现状与效率优化路径研究
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4.山东第一医科大学第一附属医院(山东省千佛山医院),山东 济南 250014, bio={"content":"

王灵婷(2001—),女,硕士在读,研究方向:卫生政策与卫生事业管理

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王灵婷(2001—),女,硕士在读,研究方向:卫生政策与卫生事业管理

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王灵婷1, 4, 谭振磊2, 王家琛2, 刘天征2, 李培龙2, 汤朝阳2, 肖雪3, 孙经杰2, 杜杨4 , 刘丽媛2
作者信息
  • 1.山东第一医科大学(山东省医学科学院)公共卫生与健康管理学院,山东 济南 250117
  • 2.山东省卫生健康委员会医疗管理服务中心(卫生统计评价办公室),山东 济南 250000
  • 3.山东第二医科大学管理学院
  • 4.山东第一医科大学第一附属医院(山东省千佛山医院),山东 济南 250014
通讯作者:
杜杨,E-mail:;
刘丽媛,E-mail:;
杜杨与刘丽媛为共同通信作者
作者简介:

王灵婷(2001—),女,硕士在读,研究方向:卫生政策与卫生事业管理

Configurational analysis of mental health resource allocation and efficiency optimization pathways in Province S: an fsQCA study
Ling-ting WANG1, 4, Zhen-lei TAN2, Jia-chen WANG2, Tian-zheng LIU2, Pei-long LI2, Chao-yang TANG2, Xue XIAO3, Jing-jie SUN2, Yang DU4 , Li-yuan LIU2
Affiliations
  • School of Public Health and Health Management, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
出版时间: 2026-05-10 doi: 10.20043/j.cnki.MPM.202511469
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目的

分析S省精神卫生资源配置现状及效率优化路径,为优化全省精神卫生资源配置,提升资源利用效率提供实证依据。

方法

基于2024年S省16个地市的精神卫生资源数据,运用数据包络分析(data envelopment analysis,DEA)测算资源配置效率,并通过模糊集定性比较分析法得出不同效率水平的条件组态。

结果

S省精神卫生资源总体呈现“增长快而分布不均”的特征。16个地市中,仅JN、DY、WF和WH 4市达到DEA强有效,综合效率均值为0.829。识别出三条高效率配置路径,分别为经济支撑型、服务驱动型和资源整合型,其总体一致性为0.808;同时识别出一条低效率配置路径,即投入失衡型,其总体一致性为0.907。

结论

S省精神卫生资源配置效率整体仍有较大提升空间。各地市可根据不同组态特征,结合本地实际优化资源布局与供给模式,因地制宜探索适配路径,从而提升全省精神卫生服务体系整体效能。

精神卫生资源  /  资源配置  /  效率优化  /  数据包络分析  /  模糊集定性比较分析
Objective

To analyze the current status and efficiency optimization pathways of mental health resource allocation in Province S and to provide empirical evidence for optimizing provincial resource allocation and improving resource utilization efficiency.

Methods

Based on mental health resource data from 16 prefecture-level cities in Province S in 2024, this study employed Data Envelopment Analysis (DEA) to evaluate the efficiency of resource allocation and used Fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify configurational conditions associated with different efficiency levels.

Results

Mental health resources in Province S have exhibited rapid growth but remain unevenly distributed overall. Among the 16 prefecture-level cities in Province S, only four cities, namely JN, DY, WF, and WH achieved strong DEA efficiency, with an average overall efficiency score of 0.829. Three high-efficiency configurational pathways were identified, namely the economic support-oriented, service-driven, and resource integration-oriented types, with an overall consistency of 0.808. In addition, one low-efficiency pathway, characterized by input imbalance, was identified, with an overall consistency of 0.907.

Conclusion

The overall efficiency of mental health resource allocation in Province S still has considerable room for improvement. Prefecture-level cities can optimize the layout and supply patterns of mental health resources according to their specific configurational characteristics and local conditions, and explore context-specific pathways to enhance the overall performance of the provincial mental health service system.

Mental health resources  /  Resource allocation  /  Efficiency optimization  /  Data envelopment analysis  /  Fuzzy-set qualitative comparative analysis
王灵婷, 谭振磊, 王家琛, 刘天征, 李培龙, 汤朝阳, 肖雪, 孙经杰, 杜杨, 刘丽媛. 基于fsQCA组态视角的S省精神卫生资源配置现状与效率优化路径研究. 现代预防医学, 2026 , 53 (9) : 1644 -1650 . DOI: 10.20043/j.cnki.MPM.202511469
Ling-ting WANG, Zhen-lei TAN, Jia-chen WANG, Tian-zheng LIU, Pei-long LI, Chao-yang TANG, Xue XIAO, Jing-jie SUN, Yang DU, Li-yuan LIU. Configurational analysis of mental health resource allocation and efficiency optimization pathways in Province S: an fsQCA study[J]. Modern Preventive Medicine, 2026 , 53 (9) : 1644 -1650 . DOI: 10.20043/j.cnki.MPM.202511469
中国精神卫生调查(China Mental Health Survey,CMHS)显示,我国成年人精神障碍的加权终生患病率为16.6%,其中以焦虑障碍、心境障碍和物质使用障碍最为常见[1]。面对日益严峻的精神障碍防治压力,国家卫生健康委员会在《关于开展“儿科和精神卫生服务年”(2025—2027年)的通知》中明确提出,应提升精神卫生服务的可及性,加强精神卫生服务体系建设[2]。在此背景下,S省精神卫生服务需求持续攀升,但省内精神卫生资源配置仍存在明显不均衡,优质资源主要集中于经济发达地区,基层供给相对薄弱,制约了资源配置的整体效率[3-4]。因此,如何实现省内精神卫生资源的合理配置与高效供给,成为亟须解决的现实问题。
相关研究表明,精神卫生资源配置状况既受床位规模、人员结构和服务利用等内部资源因素影响[5],也与经济发展水平、财政投入及制度保障等外部环境因素密切相关[6]。现有研究多采用数据包络分析(data envelopment analysis,DEA)对精神卫生资源配置效率进行测算,并从资源差异、规模结构与配置特征等方面展开分析[5,7-8],整体上侧重内部因素视角,难以系统揭示内外因素在不同条件组合下对效率形成的综合影响。相比之下,模糊集定性比较分析(fuzzy-set qualitative comparative analysis, fsQCA)能够从组态视角识别多种条件的组合效应,揭示不同配置结果的多元路径,已在卫生资源配置研究中展现良好的应用潜力[9],但缺乏关于精神卫生领域,尤其是针对省域内部差异的研究。基于此,本研究以S省为例,系统分析精神卫生资源配置现状,并综合运用DEA与fsQCA方法,探讨不同效率水平的形成路径,以期为优化全省精神卫生资源配置结构、提升精神卫生服务体系效能提供实证依据和政策参考。
数据主要来源于S省的《卫生健康统计年鉴(2021—2024)》《2024统计手册》以及2020—2024年卫生健康统计年报数据库。研究对象为依法取得《医疗机构执业许可证》的三类机构:(1)设有精神科的综合医院;(2)精神专科医院;(3)承担精神障碍防治任务的专业公共卫生机构。
1.2.1 DEA是一种基于线性规划的非参数分析方法,用于在多投入、多产出条件下评价决策单元的相对效率[10]。本研究采用DEA中的规模报酬不变(Charnes-Cooper-Rhodes,CCR)模型和规模报酬可变(Banker-Charnes-Cooper,BCC)模型,对S省精神卫生资源配置效率进行测算。
1.2.2 fsQCA是一种基于集合论的组态分析方法,能够识别不同条件组合对结果变量的多路径影响机制,常用于中小样本研究[11]。本研究采用fsQCA方法探讨S省精神卫生资源配置效率的具体提升路径。
1.3.1 结果变量 结合相关文献[5,12]和DEA方法的基本要求,本研究从资源投入与服务产出两个维度构建指标体系。投入指标包括实有床位数、执业(助理)医师数和注册护士数;产出指标包括年门急诊人次、年出院人数和医疗收入。依据上述指标体系,采用DEA法测算精神卫生资源配置的综合效率值,将其作为结果变量。
1.3.2 条件变量 综合考虑已有研究[6,13],结合fsQCA方法的适用性,并兼顾数据的可获得性与代表性,本研究从社会经济、政府支持、设施基础、人力资源及服务利用五个方面选取条件变量,包括人均GDP(X1)、人均一般公共预算支出(X2)、每千人口床位数(X3)、每千人口卫生技术人员数(X4)以及每千人口门急诊量(X5)。
采用SPSS 27.0进行统计分析,以中位数及四分位间距P50(P25,P75)描述S省精神卫生资源配置现状;DEA效率采用DEAP 2.1软件完成,fsQCA使用fsQCA 4.0软件实施。检验水准α= 0.05。
2.1.1 2020—2024年S省精神卫生医疗机构基本情况 截至2024年,S省精神卫生医疗机构数量由2020年的282家增至468家,增长65.96%;实有床位数、平均门急诊人次和平均出院人数分别增长77.86%、71.75%和109.80%。见图1~4。
2.1.2 2024年S省精神卫生资源现状基本描述 2024年,S省精神卫生医疗机构共468家,剔除无实有床位数及仅提供门诊服务的机构后,纳入分析的机构为266家。全省政府部门举办机构占34.96%,企业及个人占59.77%,社会组织占5.26%。按机构层级划分,省级机构以门急诊服务为主,地市级和区县级机构主要承担住院与出院服务,床位及人员配置相对集中。按机构级别划分,三级机构在床位规模、服务量和人员配置方面优势突出,二级机构相对不足,一级及未评级机构居中。见表1。
2024年S省精神卫生资源配置的综合效率、纯技术效率和规模效率均值分别为0.829、0.887和0.938。JN、DY、WF和WH 4个地市为DEA强有效,呈规模报酬不变;QD市和HZ市为DEA弱有效,呈规模报酬递减;其余10个地市为非DEA有效,其中ZB市和RZ市呈规模报酬递增,ZZ、YT、JNi等市呈规模报酬递减。见表2。
2.3.1 各变量数据校准 本研究采用四分位数法设定校准阈值,将完全隶属、交叉点和完全不隶属的三个锚点分别设为原始数据的95%、50%和5%分位数,并据此计算各变量的模糊集隶属度(0~1)。鉴于隶属度为0.50可能导致相似数据被排除,对所有0.50的隶属度统一增加0.001。校准结果见表3。
2.3.2 单个前因条件的必要性分析 必要性分析用于检验各前因条件是否构成结果发生的必要条件[14]。当一致性大于0.9时,可认定该条件为必要条件。本研究结果显示,各前因条件的一致性均未达到该阈值,表明精神卫生资源配置效率不存在单一的必要条件。见表4。
2.3.3 前因条件组态分析 fsQCA结果显示,各单个解及总体解的一致性均高于0.8,表明4个组态均构成各地市精神卫生资源配置结果的充分条件。其中,高效配置组态的总体覆盖度为0.519,可解释51.9%的高效率案例;低效配置组态的总体一致性为0.907,表明其对低效率配置结果具有较强解释力。
本研究识别出实现高效配置的三条核心路径:(1)H1经济支撑型:在社会经济水平较高、设施基础相对完善的条件下,即使政府支持不足、人力资源和服务利用有限,仍可实现效率提升,代表地区包括JN、DY、WF、WH等市。(2)H2服务驱动型:在服务利用水平较高、设施基础与人力资源相对充足的条件下,即使社会经济水平较低、政府支持不足,亦可实现效率提升,代表地区包括ZZ、JNi、RZ等市。(3)H3资源整合型:在政府支持和人力资源充足、社会经济水平和服务利用相对较好的条件下,即使设施基础不足,也能实现效率提升,代表地区包括QD、LY、HZ等市。同时,识别出一条低效率配置路径,即L投入失衡型:当政府支持作为核心条件存在,而社会经济水平较低,且设施基础、人力资源和服务利用相对不足时,更可能导致低效率,代表地区包括YT、TA、BZ等市。见表5。
2.3.4 稳健性检验 为检验结果的稳健性,本研究将fsQCA中的一致性阈值从0.80提高至0.85,其余条件保持不变。结果显示,识别出的条件组合模式与调整前一致,表明分析结果具有较强的稳健性。
2020—2024年,S省精神卫生资源在机构数量、床位规模和服务利用方面持续增长,但整体呈现“增长快而分布不均”的特征。全省综合效率均值为0.829,表明资源配置尚未达到最优,其内部可能存在结构性失衡。为此,应构建“省级统筹、地市引领、基层支撑”的整合式服务体系,通过强化分级协同与资源共享,推动优质资源有序下沉[15]。同时,还须注意到地区间的异质性,对于ZB、RZ等呈规模报酬递增的地市,应加大人力和设施投入,强化专业培训与政策支持[10];对于QD、ZZ、YT等呈规模报酬递减的地市,则应侧重提高资源利用效率和推进管理制度创新,防止资源冗余与低效扩张[16]。
经济支撑型路径(H1)表明,高水平经济发展是实现精神卫生资源高效配置的重要基础。研究发现,S省经济发达地市依托相对完善的设施基础和管理体系,具备较强的资源承载与运行保障能力。以JN市为例,其医疗资源集聚和信息化管理优势有助于提升资源利用效率;DY、WF和WH等市则凭借较强的产业基础和相对完善的服务体系,在资源统筹配置和服务网络建设方面具有良好条件。因此,H1类地市应以优化经济结构为核心,加强精神卫生领域的财政投入,并通过优化投入结构提升财政支出效应[17];推进按“病种付费”为主的医保支付方式改革,增强医保基金的使用效能[18];引导社会资本有序参与精神卫生服务体系建设,重点支持高质量专科服务与智慧医疗发展。
服务驱动型路径(H2)表明,服务利用水平的有效提升是推动精神卫生资源高效配置的关键。研究显示,S省部分经济基础相对薄弱的地市,通过优化服务体系,仍可实现较高配置效率。以ZZ市和RZ市为例,两地以转诊协作和服务衔接为主要特征,有效提升了服务的连续性;JNi市通过加强基层服务与专科诊疗协同,逐步完善综合服务供给体系。因此,H2类地市应以提升服务能力为重点,强化组织管理与专科协作,增强服务可及性与患者体验[19]。同时夯实基层医疗机构在筛查、随访和康复管理中的功能定位,推动“防-治-康”一体化发展[20]。
资源整合型路径(H3)表明,财政支持与人员配置的协同作用是实现精神卫生资源高效配置的重要驱动力。研究发现,S省部分地市虽存在一定的设施基础不足,但能通过强化资源整合,实现较高的整体运作效率。具体而言,QD市依托三级精神卫生专科机构的资源优势,强化跨机构协同;LY市通过医共体建设推动资源统筹配置;HZ市则借助医联体协作和省级政策支持,为资源整合提供制度保障。因此,H3类地市应以系统协同为核心,推动跨区域、跨机构的精神卫生资源整合,通过医联体和医共体机制实现人力与技术共享。同时加快建设数据驱动的智能化管理体系,推进远程会诊、在线随访和智能监测等新型服务模式应用,以扩大优质医疗服务的覆盖范围[21]。
投入失衡型路径(L)表明,在社会经济水平和服务能力相对不足的条件下,单纯增加财政投入难以有效提升精神卫生资源配置效率。以YT、TA和BZ等市为例,这些地区在精神卫生设施基础、服务网络建设和专业人才供给方面存在明显短板,导致财政投入与服务能力结构性错配,资源转化效率受限,呈现“高投入-低产出”特征。因此,L类地市在保持财政投入强度的同时,应以人群健康需求为导向,构建以战略性购买为核心的精准投入机制,提高财政资金使用效率[22];依托数字化平台推进精神卫生服务网络整合,促进不同层级心理健康服务的协同衔接[23];并通过分层分类的人才培养与激励支持机制,系统提升精神卫生服务能力,促进财政投入向服务效能高效转化。
综上所述,S省精神卫生资源配置正处于由“规模扩张”向“效率提升”转型的关键阶段。fsQCA分析结果表明,高效率配置可通过多条路径实现,并在不同地区与发展阶段呈现出结构性互补特征;同时低效率路径提示单纯依赖财政投入难以有效提升资源配置效率。未来,应坚持以“结构重构-服务提质-协同创新”为发展主线,结合各地市经济基础与服务能力差异,因地制宜优化资源配置与服务模式,推动全省精神卫生服务体系向高质量、均衡化和可持续方向发展。
  • 山东省卫生健康委员会医疗管理服务中心科研创新博士基金项目(2025B02)
  • 潍坊市中央财政支持公立医院改革与高质量发展示范项目委托课题(2024WF07)
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2026年第53卷第9期
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doi: 10.20043/j.cnki.MPM.202511469
  • 接收时间:2025-11-26
  • 首发时间:2026-11-02
  • 出版时间:2026-05-10
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  • 收稿日期:2025-11-26
基金
山东省卫生健康委员会医疗管理服务中心科研创新博士基金项目(2025B02)
潍坊市中央财政支持公立医院改革与高质量发展示范项目委托课题(2024WF07)
作者信息
    1.山东第一医科大学(山东省医学科学院)公共卫生与健康管理学院,山东 济南 250117
    2.山东省卫生健康委员会医疗管理服务中心(卫生统计评价办公室),山东 济南 250000
    3.山东第二医科大学管理学院
    4.山东第一医科大学第一附属医院(山东省千佛山医院),山东 济南 250014

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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
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占总种数比例
Percentage of
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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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