Article(id=1311350468308202091, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350363295417204, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202601435, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1769443200000, receivedDateStr=2026-01-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579915357, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579915357, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579915357, creator=13701087609, updateTime=1790579915357, updator=13701087609, issue=Issue{id=1311350363295417204, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='16', pageStart='2881', pageEnd='3072', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790579890321, creator='13701087609', updateTime=1790584112451, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311368072267653663, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350363295417204, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311368072267653664, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350363295417204, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2934, endPage=2940, ext={EN=ArticleExt(id=1311350468492751468, articleId=1311350468308202091, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Influencing factors of occupational stress among workers in key industries in Bijie City on Bayesian network, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the influencing factors of occupational stress and their relationships among workers in key industries of Bijie City, so as to provide scientific reference for the construction of occupational health intervention system.

Methods

A total of 1 986 workers from 8 key industries in Bijie City were enrolled by stratified random sampling. Relevant information on occupational stress was collected by the National Key Population Occupational Health Literacy Monitoring Questionnaire. SPSS 26.0 was used to screen influencing factors, and GeNIe 4.0 was adopted to construct a Bayesian network model for backward reasoning and sensitivity analysis.

Results

The detection rate of occupational stress among workers in key industries of Bijie City was 29.88%.Multivariate analysis showed that females (OR=1.335, 95%CI:1.005-1.774), employees in private enterprises (OR=2.467, 95%CI:1.362-4.469), night shift workers (OR=1.876, 95%CI:1.478-2.382), workers with self-rated general health status (OR=1.710, 95%CI:1.098-2.662), and those lacking occupational health literacy (OR=2.562, 95%CI:2.004-3.276) had an increased risk of occupational stress. The Bayesian network model consisted of 9 nodes and 14 directed edges. Industry category, enterprise nature, weekly working hours, night shift, health status and occupational health literacy were direct influencing factors, while gender was an indirect influencing factor. The classification accuracy of the model was 77.12%, with an AUC of 0.823 (95%CI:0.80-0.85), indicating good predictive efficiency and model fitting performance. Backward reasoning identified 6 core risk nodes, and industry category was the most sensitive variable.

Conclusion

Industry category, enterprise nature, weekly working hours, night shift, health status and occupational health literacy can be taken as the intervention targets for the prevention and control of occupational stress.

, authors=Qian YAO1, Jing-yuan YANG1, Yao YANG2, Fang-xu TUO2, Long XU2, Miao YU1, Yong LIU1, 3, authorsList=Qian YAO, Jing-yuan YANG, Yao YANG, Fang-xu TUO, Long XU, Miao YU, Yong LIU, authorCompany=null, correspAuthors=Yong 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=1311350469407109749, articleId=1311350468308202091, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于贝叶斯网络的毕节市重点行业劳动者职业紧张影响因素分析, columnId=1228016571273113811, journalTitle=现代预防医学, columnName=环境与职业卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探究毕节市重点行业劳动者职业紧张的影响因素及相互关系,为构建职业健康干预体系提供科学参考。

方法

采用分层随机抽样选取毕节市8个重点行业1 986名劳动者,通过《全国重点人群职业健康素养监测调查个人问卷》收集职业紧张相关信息,运用SPSS 26.0筛选影响因素,GeNIe 4.0构建贝叶斯网络模型,并进行逆向推理及敏感性分析。

结果

毕节市重点行业劳动者职业紧张检出率29.88%。多因素分析显示:女性(OR=1.335,95%CI:1.005~1.774)、民营企业(OR=2.467,95%CI:1.362~4.469)、上夜班(OR=1.876,95%CI:1.478~2.382)、自评健康一般(OR=1.710,95%CI:1.098~2.662)及不具备职业健康素养(OR=2.562,95%CI:2.004~3.276)发生职业紧张的风险增加。贝叶斯网络模型含9个节点、14条有向边,行业类别、企业性质、周工作时长、夜班、健康状态、职业健康素养为直接影响因素,性别为间接影响因素;模型分类准确率77.12%,AUC=0.823(95%CI:0.80~0.85),预测效能与拟合效果良好。逆向推理识别出6个核心风险节点,行业类别是最敏感变量。

结论

行业类别、企业性质、周工作时长、夜班、健康状态、职业健康素养为关键影响因素的干预靶点。

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姚茜(2000—),女,硕士在读,研究方向:疾病预防与控制

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刘勇,E-mail:
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Investigation and study on occupational health literacy among key industry populations in Ili Kazakh Autonomous Prefecture[J]. Bulletin of Disease Control & Prevention(China), 2026, 41(1): 28-33.(In Chinese), articleTitle=Investigation and study on occupational health literacy among key industry populations in Ili Kazakh Autonomous Prefecture, refAbstract=null), Reference(id=1311350481029526237, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, doi=null, pmid=null, pmcid=null, year=2024, volume=42, issue=2, pageStart=222, pageEnd=226, url=null, language=null, rfNumber=[22], rfOrder=37, authorNames=赵宏诚, 潘引君, 刘晓晓, journalName=职业卫生与应急救援, refType=null, unstructuredReference=赵宏诚,潘引君,刘晓晓,等.上海市青浦区两个重点行业劳动者职业健康素养水平调查[J].职业卫生与应急救援,2024,42(2)222-226., articleTitle=上海市青浦区两个重点行业劳动者职业健康素养水平调查, refAbstract=null), Reference(id=1311350481113412318, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, doi=null, pmid=null, pmcid=null, year=2024, volume=42, issue=2, pageStart=222, pageEnd=226, url=null, language=null, rfNumber=[22], rfOrder=38, authorNames=Zhao HC, Pan YJ, Liu XX, journalName=Occupational Health and Emergency Rescue, refType=null, unstructuredReference=Zhao HC, Pan YJ, Liu XX, et al. Investigation on the level of occupational health literacy among workers in two key industries in Qingpu District of Shanghai[J]. Occupational Health and Emergency Rescue, 2024, 42(2): 222-226.(in Chinese), articleTitle=Investigation on the level of occupational health literacy among workers in two key industries in Qingpu District of Shanghai, refAbstract=null), Reference(id=1311350481180521183, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, doi=null, pmid=null, pmcid=null, year=2023, volume=23, issue=1, pageStart=1223, pageEnd=null, url=null, language=null, rfNumber=[23], rfOrder=39, authorNames=Wang JL, Mo CB, Sheng QJ, journalName=BMC Public Health, refType=null, unstructuredReference=Wang JL, Mo CB, Sheng QJ, et al. Association of length of service and job category with occupational health literacy of port employees in Shenzhen, China[J]. BMC Public Health, 2023, 23(1): 1223., articleTitle=Association of length of service and job category with occupational health literacy of port employees in Shenzhen, China, refAbstract=null)], funds=[Fund(id=1311350476550009527, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, awardId=毕科联合〔2025〕150号, language=CN, fundingSource=毕节市科学技术局科学技术联合基金(毕科联合〔2025〕150号), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311350469637796470, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, xref=1., ext=[AuthorCompanyExt(id=1311350469646185079, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, companyId=1311350469637796470, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, School of Public Health and Health Science, Guizhou Medical University, Guiyang, Guizhou 561113, China), AuthorCompanyExt(id=1311350469654573688, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, companyId=1311350469637796470, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.贵州医科大学公共卫生与健康学院环境污染与疾病监控教育部重点实验室,贵州 贵阳 561113)]), AuthorCompany(id=1311350469721682553, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, xref=2., ext=[AuthorCompanyExt(id=1311350469730071162, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, companyId=1311350469721682553, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.毕节市疾病预防控制中心)]), AuthorCompany(id=1311350469784597115, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, xref=3., ext=[AuthorCompanyExt(id=1311350469792985724, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, companyId=1311350469784597115, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.毕节医学高等专科学校)])], figs=[ArticleFig(id=1311350473609802407, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Fig.1, caption=Bayesian network model of influencing factors for occupational stress among workers in key industries of Bijie city, figureFileSmall=IcMjMW/48dNQjBhwJyY9qw==, figureFileBig=9W2vESfOlyjR9807Z+V3BA==, tableContent=null), ArticleFig(id=1311350473668522664, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=图1, caption=毕节市重点行业劳动者职业紧张影响因素的贝叶斯网络模型, figureFileSmall=IcMjMW/48dNQjBhwJyY9qw==, figureFileBig=9W2vESfOlyjR9807Z+V3BA==, tableContent=null), ArticleFig(id=1311350473836294825, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Fig.2, caption=ROC curve of the bayesian network prediction model for occupational stress among workers in key industries of Bijie city, figureFileSmall=BCindrrBuVh8OszpQzhECA==, figureFileBig=cLXVX2kIGbUa4bTxyKLccA==, tableContent=null), ArticleFig(id=1311350473911792298, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=图2, caption=毕节市重点行业劳动者职业紧张的贝叶斯网络预测模型ROC曲线图, figureFileSmall=BCindrrBuVh8OszpQzhECA==, figureFileBig=cLXVX2kIGbUa4bTxyKLccA==, tableContent=null), ArticleFig(id=1311350473974706859, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Fig.3, caption=Sensitivity analysis results of the model, figureFileSmall=9JII6MFIDEsZ0htixl5SyQ==, figureFileBig=rGIUd5rXtu3LqONA1+8euA==, tableContent=null), ArticleFig(id=1311350474046010028, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=图3, caption=贝叶斯模型敏感性分析结果, figureFileSmall=9JII6MFIDEsZ0htixl5SyQ==, figureFileBig=rGIUd5rXtu3LqONA1+8euA==, tableContent=null), ArticleFig(id=1311350474129896109, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Table 1, caption=

Distribution of occupational stress prevalence among workers in Bijie city

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总例数
(构成比%)
职业紧张检出人数
(检出率%)
χ2值P值
性别22.753<0.001
男1 286(68.73)428(33.28)
女585(31.27)131(22.39)
年龄(岁)1.4360.697
<30370(19.78)104(28.11)
30~<40717(38.32)225(31.38)
40~<50522(27.90)153(29.31)
≥50262(14.00)77(29.39)
民族7.7270.259
汉族1 639(87.60)491(29.96)
少数民族232(12.40)68(29.31)
婚姻状况3.5800.167
未婚304(16.25)84(27.63)
已婚1 456(77.82)449(30.84)
离婚/丧偶111(5.93)26(23.42)
文化程度13.924<0.001
小学及以下154(8.23)47(30.52)
初中494(26.40)178(36.03)
高中/职高/中专337(18.01)97(28.78)
大专306(16.36)87(28.43)
大学及以上580(31.00)150(25.86)
实际平均月收入(元)4.6690.198
少于3 000293(15.66)93(31.74)
3 000~4 999614(32.82)195(31.76)
5 000~6 999663(35.44)195(29.41)
7 000及以上301(16.08)76(25.25)
户口性质5.3870.068
非农业户口526(28.11)146(27.76)
农业户口1 035(55.32)304(29.37)
其他310(16.56)109(35.16)
行业类别43.668<0.001
煤碳开采和洗选业203(10.85)74(36.45)
非金属矿物制品业197(10.53)47(23.86)
电力/热力/燃气及水生产和供应业302(16.14)99(32.78)
医疗卫生业249(13.31)50(20.08)
教育业272(14.54)70(25.74)
环境卫生业87(4.65)18(20.69)
交通运输业261(13.95)78(29.89)
快递及外卖配送业300(16.03)123(41.00)
企业性质17.0980.001
国有企业223(11.92)74(33.18)
民营企业135(7.22)39(28.89)
事业单位521(27.85)120(23.03)
其他992(53.01)326(32.86)
企业规模(人)4.5080.105
20~2991 051(56.17)331(31.49)
300~999503(26.88)132(26.24)
≥1 000317(16.94)96(30.28)
工龄(年)5.7390.125
<2651(34.79)178(27.34)
2~5480(25.65)162(33.75)
6~10243(12.99)75(30.86)
≥11497(26.57)144(28.97)
周工作时长(h)52.163<0.001
≤40465(24.85)126(27.10)
41~44393(21.00)100(25.45)
45~48313(16.73)81(25.88)
49~54282(15.07)68(24.11)
≥55418(22.34)184(44.02)
夜班31.705<0.001
否1 052(56.23)259(24.62)
是819(43.77)300(36.63)
健康状态9.1570.010
差115(6.15)48(41.74)
一般677(36.18)206(30.43)
好1 079(57.67)305(28.27)
一年内因病请假休工情况31.591<0.001
没有1 379(73.70)364(26.40)
不足3天279(14.92)105(37.63)
3~7天122(6.52)51(41.80)
7天以上91(4.86)39(42.86)
职业健康素养76.711<0.001
不具备1 100(58.79)414(37.64)
具备771(41.21)145(18.81)
), ArticleFig(id=1311350474209587886, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=表1, caption=

毕节市劳动者职业紧张检出率分布情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总例数
(构成比%)
职业紧张检出人数
(检出率%)
χ2值P值
性别22.753<0.001
男1 286(68.73)428(33.28)
女585(31.27)131(22.39)
年龄(岁)1.4360.697
<30370(19.78)104(28.11)
30~<40717(38.32)225(31.38)
40~<50522(27.90)153(29.31)
≥50262(14.00)77(29.39)
民族7.7270.259
汉族1 639(87.60)491(29.96)
少数民族232(12.40)68(29.31)
婚姻状况3.5800.167
未婚304(16.25)84(27.63)
已婚1 456(77.82)449(30.84)
离婚/丧偶111(5.93)26(23.42)
文化程度13.924<0.001
小学及以下154(8.23)47(30.52)
初中494(26.40)178(36.03)
高中/职高/中专337(18.01)97(28.78)
大专306(16.36)87(28.43)
大学及以上580(31.00)150(25.86)
实际平均月收入(元)4.6690.198
少于3 000293(15.66)93(31.74)
3 000~4 999614(32.82)195(31.76)
5 000~6 999663(35.44)195(29.41)
7 000及以上301(16.08)76(25.25)
户口性质5.3870.068
非农业户口526(28.11)146(27.76)
农业户口1 035(55.32)304(29.37)
其他310(16.56)109(35.16)
行业类别43.668<0.001
煤碳开采和洗选业203(10.85)74(36.45)
非金属矿物制品业197(10.53)47(23.86)
电力/热力/燃气及水生产和供应业302(16.14)99(32.78)
医疗卫生业249(13.31)50(20.08)
教育业272(14.54)70(25.74)
环境卫生业87(4.65)18(20.69)
交通运输业261(13.95)78(29.89)
快递及外卖配送业300(16.03)123(41.00)
企业性质17.0980.001
国有企业223(11.92)74(33.18)
民营企业135(7.22)39(28.89)
事业单位521(27.85)120(23.03)
其他992(53.01)326(32.86)
企业规模(人)4.5080.105
20~2991 051(56.17)331(31.49)
300~999503(26.88)132(26.24)
≥1 000317(16.94)96(30.28)
工龄(年)5.7390.125
<2651(34.79)178(27.34)
2~5480(25.65)162(33.75)
6~10243(12.99)75(30.86)
≥11497(26.57)144(28.97)
周工作时长(h)52.163<0.001
≤40465(24.85)126(27.10)
41~44393(21.00)100(25.45)
45~48313(16.73)81(25.88)
49~54282(15.07)68(24.11)
≥55418(22.34)184(44.02)
夜班31.705<0.001
否1 052(56.23)259(24.62)
是819(43.77)300(36.63)
健康状态9.1570.010
差115(6.15)48(41.74)
一般677(36.18)206(30.43)
好1 079(57.67)305(28.27)
一年内因病请假休工情况31.591<0.001
没有1 379(73.70)364(26.40)
不足3天279(14.92)105(37.63)
3~7天122(6.52)51(41.80)
7天以上91(4.86)39(42.86)
职业健康素养76.711<0.001
不具备1 100(58.79)414(37.64)
具备771(41.21)145(18.81)
), ArticleFig(id=1311350474310251183, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Table 2, caption=

Coding of variables

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组及赋值
自变量
性别男=1,女=2;
文化程度小学及以下=1,初中=2,高中/职高/中专=3,大专=4,大学及以上=5;
行业类别煤炭开采和洗选业=1,金属矿物制品业=2,电力/热力/燃气及水生产和供应业=3,医疗卫生业=4,教育业=5,环境卫生业=6,交通运输业=7,快递/外卖配送业=8;
企业性质国有企业=1,民营企业=2,事业单位=3,其他=4;1
周工作时间≤40 h=1,41~44 h=2,45~48 h=3,49~54 h=4,≥55 h=5;
夜班否=1,是=2;
健康状态差=1,一般=2,好=3;
一年内因病请假休工情况无=1,少于3天=2,3天至1周=3,超过1周=4;
职业健康素养不具备=1,具备=2。
因变量
职业紧张否=1,是=2。
), ArticleFig(id=1311350474398331568, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=表2, caption=

变量赋值情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组及赋值
自变量
性别男=1,女=2;
文化程度小学及以下=1,初中=2,高中/职高/中专=3,大专=4,大学及以上=5;
行业类别煤炭开采和洗选业=1,金属矿物制品业=2,电力/热力/燃气及水生产和供应业=3,医疗卫生业=4,教育业=5,环境卫生业=6,交通运输业=7,快递/外卖配送业=8;
企业性质国有企业=1,民营企业=2,事业单位=3,其他=4;1
周工作时间≤40 h=1,41~44 h=2,45~48 h=3,49~54 h=4,≥55 h=5;
夜班否=1,是=2;
健康状态差=1,一般=2,好=3;
一年内因病请假休工情况无=1,少于3天=2,3天至1周=3,超过1周=4;
职业健康素养不具备=1,具备=2。
因变量
职业紧张否=1,是=2。
), ArticleFig(id=1311350474503189169, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Table 3, caption=

Logistic regression analysis of influencing factors for occupational stress among workers in key industries of Bijie City

, figureFileSmall=null, figureFileBig=null, tableContent=
变量偏回归系数标准误wald χ2值P值OR(95%CI)
性别
男1
女0.2890.1453.9810.0461.335(1.005~1.774)
行业类别
煤碳开采和洗选业1
非金属矿物制品业-0.1990.2030.9580.3280.819(0.550~1.221)
电力/热力/燃气及水生产和供应业-1.1120.30313.432<0.0010.329(0.181~0.596)
医疗卫生业-1.1170.31512.553<0.0010.327(0.176~0.607)
教育业-0.5240.2315.1260.0240.592(0.376~0.932)
环境卫生业-0.7250.20812.145<0.0010.484(0.322~0.728)
交通运输业-1.6330.44213.659<0.0010.195(0.082~0.464)
快递及外卖配送业-0.4210.1974.5400.0330.657(0.446~0.967)
企业性质
国有企业1
民营企业0.9030.3038.8770.0032.467(1.362~4.469)
事业单位0.2300.3150.5330.4651.258(0.679~2.332)
周工作时长(h)
≤401
41~44-0.5590.15413.125<0.0010.572(0.422~0.774)
45~48-0.5130.1689.2790.0020.599(0.431~0.833)
49~54-0.7000.17715.728<0.0010.497(0.351~0.702)
≥55-0.8250.18020.927<0.0010.438(0.308~0.624)
夜班
否1
是0.6290.12226.676<0.0011.876(1.478~2.382)
健康状态
差1
一般0.5360.2265.6310.0181.710(1.098~2.662)
好0.0090.1210.0060.9381.009(0.796~1.280)
一年内,因病请假休工情况
没有1
不足3天-0.6730.2397.9540.0050.510(0.320~0.814)
3~7天-0.1840.2630.4890.4840.832(0.497~1.393)
7天以上-0.2110.2990.4990.4800.810(0.451~1.455)
职业健康素养
具备1
不具备0.9410.12556.235<0.0012.562(2.004~3.276)
常量-0.5570.3282.8860.0890.573(-)
), ArticleFig(id=1311350474574492338, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=表3, caption=

毕节市重点行业劳动者职业紧张影响因素的logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量偏回归系数标准误wald χ2值P值OR(95%CI)
性别
男1
女0.2890.1453.9810.0461.335(1.005~1.774)
行业类别
煤碳开采和洗选业1
非金属矿物制品业-0.1990.2030.9580.3280.819(0.550~1.221)
电力/热力/燃气及水生产和供应业-1.1120.30313.432<0.0010.329(0.181~0.596)
医疗卫生业-1.1170.31512.553<0.0010.327(0.176~0.607)
教育业-0.5240.2315.1260.0240.592(0.376~0.932)
环境卫生业-0.7250.20812.145<0.0010.484(0.322~0.728)
交通运输业-1.6330.44213.659<0.0010.195(0.082~0.464)
快递及外卖配送业-0.4210.1974.5400.0330.657(0.446~0.967)
企业性质
国有企业1
民营企业0.9030.3038.8770.0032.467(1.362~4.469)
事业单位0.2300.3150.5330.4651.258(0.679~2.332)
周工作时长(h)
≤401
41~44-0.5590.15413.125<0.0010.572(0.422~0.774)
45~48-0.5130.1689.2790.0020.599(0.431~0.833)
49~54-0.7000.17715.728<0.0010.497(0.351~0.702)
≥55-0.8250.18020.927<0.0010.438(0.308~0.624)
夜班
否1
是0.6290.12226.676<0.0011.876(1.478~2.382)
健康状态
差1
一般0.5360.2265.6310.0181.710(1.098~2.662)
好0.0090.1210.0060.9381.009(0.796~1.280)
一年内,因病请假休工情况
没有1
不足3天-0.6730.2397.9540.0050.510(0.320~0.814)
3~7天-0.1840.2630.4890.4840.832(0.497~1.393)
7天以上-0.2110.2990.4990.4800.810(0.451~1.455)
职业健康素养
具备1
不具备0.9410.12556.235<0.0012.562(2.004~3.276)
常量-0.5570.3282.8860.0890.573(-)
), ArticleFig(id=1311350474658378419, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Table 4, caption=

Discretization grouping and assignment of variables

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组及赋值
性别Sexmale=男,female=2
行业类别 Industry_CategoryIc1=煤炭开采和洗选业,Ic2=金属矿物制品业,Ic3=电力/热力/燃气及水生产和供应业,Ic4=医疗卫生业,Ic5=教育业,Ic6=环境卫生业,Ic7=交通运输业,Ic8=快递/外卖配送业
企业性质Enterprise_typestate=国有企业,private=民营企业,inst=事业单位,other=其他
周工作时间Work_hourswh1=40 h,wh2=41~44 h,wh3=45~48 h,wh4=49~54 h,wh5=≥55 h
夜班Night_shiftns_no=否,ns_yes=是;hs_poor=差,hs_normal=一般,hs_good=好
健康状态Health_statussl0=没有,sl1=因病请假不足3天,sl2=因病请假3天到一周,sl3=
一年内因病请假休工情况Sick_leave因病请假一周以上
职业健康素养Health_literacyhl_no=不具备,hl_yes=具备=2
职业紧张Occupational_stressos_no=否,os_yes=是
), ArticleFig(id=1311350476256408244, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=表4, caption=

变量的离散化分组与赋值

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组及赋值
性别Sexmale=男,female=2
行业类别 Industry_CategoryIc1=煤炭开采和洗选业,Ic2=金属矿物制品业,Ic3=电力/热力/燃气及水生产和供应业,Ic4=医疗卫生业,Ic5=教育业,Ic6=环境卫生业,Ic7=交通运输业,Ic8=快递/外卖配送业
企业性质Enterprise_typestate=国有企业,private=民营企业,inst=事业单位,other=其他
周工作时间Work_hourswh1=40 h,wh2=41~44 h,wh3=45~48 h,wh4=49~54 h,wh5=≥55 h
夜班Night_shiftns_no=否,ns_yes=是;hs_poor=差,hs_normal=一般,hs_good=好
健康状态Health_statussl0=没有,sl1=因病请假不足3天,sl2=因病请假3天到一周,sl3=
一年内因病请假休工情况Sick_leave因病请假一周以上
职业健康素养Health_literacyhl_no=不具备,hl_yes=具备=2
职业紧张Occupational_stressos_no=否,os_yes=是
), ArticleFig(id=1311350476348682933, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=EN, label=Table 5, caption=

Inferential analysis of key influencing factors for occupational stress among workers in key industries of Bijie city

, figureFileSmall=null, figureFileBig=null, tableContent=
变量先验概率(%)逆向推理(%)敏感度指数
行业类别0.069
煤炭开采和洗选业10.853.14
非金属矿物制品业10.530.53
电力/热力/燃气及水生产和供应业16.14-0.57
医疗卫生业13.30-3.20
教育业14.53-2.01
环境卫生业4.66-0.21
交通运输业13.95-0.90
快递/外卖配送业16.033.23
周工作时长(h)0.029
≤4024.83-1.70
41~4421.00-1.28
45~4816.74-2.00
49~5415.09-0.72
≥5522.335.71
健康状态0.027
差6.261.63
一般36.180.51
好57.56-2.13
夜班0.027
否43.807.23
是56.20-7.23
职业健康素养0.027
否58.758.77
是41.25-8.77
企业性质0.026
国有企业11.972.97
民营企业7.360.62
事业单位27.84-5.22
其他52.841.80
), ArticleFig(id=1311350476428374710, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350468308202091, language=CN, label=表5, caption=

毕节市重点行业劳动者职业紧张关键影响因素推理分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量先验概率(%)逆向推理(%)敏感度指数
行业类别0.069
煤炭开采和洗选业10.853.14
非金属矿物制品业10.530.53
电力/热力/燃气及水生产和供应业16.14-0.57
医疗卫生业13.30-3.20
教育业14.53-2.01
环境卫生业4.66-0.21
交通运输业13.95-0.90
快递/外卖配送业16.033.23
周工作时长(h)0.029
≤4024.83-1.70
41~4421.00-1.28
45~4816.74-2.00
49~5415.09-0.72
≥5522.335.71
健康状态0.027
差6.261.63
一般36.180.51
好57.56-2.13
夜班0.027
否43.807.23
是56.20-7.23
职业健康素养0.027
否58.758.77
是41.25-8.77
企业性质0.026
国有企业11.972.97
民营企业7.360.62
事业单位27.84-5.22
其他52.841.80
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基于贝叶斯网络的毕节市重点行业劳动者职业紧张影响因素分析
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姚茜 1 , 杨敬源 1 , 杨瑶 2 , 庹芳旭 2 , 徐龙 2 , 余妙 1 , 刘勇 1, 3
现代预防医学 | 环境与职业卫生 2026,53(16): 2934-2940
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现代预防医学 |环境与职业卫生 2026 , 53 (16) : 2934 -2940
基于贝叶斯网络的毕节市重点行业劳动者职业紧张影响因素分析
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姚茜1, 杨敬源1, 杨瑶2, 庹芳旭2, 徐龙2, 余妙1, 刘勇1, 3
作者信息
  • 1.贵州医科大学公共卫生与健康学院环境污染与疾病监控教育部重点实验室,贵州 贵阳 561113
  • 2.毕节市疾病预防控制中心
  • 3.毕节医学高等专科学校
通讯作者:
刘勇,E-mail:
作者简介:

姚茜(2000—),女,硕士在读,研究方向:疾病预防与控制

Influencing factors of occupational stress among workers in key industries in Bijie City on Bayesian network
Qian YAO1, Jing-yuan YANG1, Yao YANG2, Fang-xu TUO2, Long XU2, Miao YU1, Yong LIU1, 3
Affiliations
  • Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, School of Public Health and Health Science, Guizhou Medical University, Guiyang, Guizhou 561113, China
出版时间: 2026-08-25 doi: 10.20043/j.cnki.MPM.202601435
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目的

探究毕节市重点行业劳动者职业紧张的影响因素及相互关系,为构建职业健康干预体系提供科学参考。

方法

采用分层随机抽样选取毕节市8个重点行业1 986名劳动者,通过《全国重点人群职业健康素养监测调查个人问卷》收集职业紧张相关信息,运用SPSS 26.0筛选影响因素,GeNIe 4.0构建贝叶斯网络模型,并进行逆向推理及敏感性分析。

结果

毕节市重点行业劳动者职业紧张检出率29.88%。多因素分析显示:女性(OR=1.335,95%CI:1.005~1.774)、民营企业(OR=2.467,95%CI:1.362~4.469)、上夜班(OR=1.876,95%CI:1.478~2.382)、自评健康一般(OR=1.710,95%CI:1.098~2.662)及不具备职业健康素养(OR=2.562,95%CI:2.004~3.276)发生职业紧张的风险增加。贝叶斯网络模型含9个节点、14条有向边,行业类别、企业性质、周工作时长、夜班、健康状态、职业健康素养为直接影响因素,性别为间接影响因素;模型分类准确率77.12%,AUC=0.823(95%CI:0.80~0.85),预测效能与拟合效果良好。逆向推理识别出6个核心风险节点,行业类别是最敏感变量。

结论

行业类别、企业性质、周工作时长、夜班、健康状态、职业健康素养为关键影响因素的干预靶点。

贝叶斯网络模型  /  职业紧张  /  二元logistic回归
Objective

To explore the influencing factors of occupational stress and their relationships among workers in key industries of Bijie City, so as to provide scientific reference for the construction of occupational health intervention system.

Methods

A total of 1 986 workers from 8 key industries in Bijie City were enrolled by stratified random sampling. Relevant information on occupational stress was collected by the National Key Population Occupational Health Literacy Monitoring Questionnaire. SPSS 26.0 was used to screen influencing factors, and GeNIe 4.0 was adopted to construct a Bayesian network model for backward reasoning and sensitivity analysis.

Results

The detection rate of occupational stress among workers in key industries of Bijie City was 29.88%.Multivariate analysis showed that females (OR=1.335, 95%CI:1.005-1.774), employees in private enterprises (OR=2.467, 95%CI:1.362-4.469), night shift workers (OR=1.876, 95%CI:1.478-2.382), workers with self-rated general health status (OR=1.710, 95%CI:1.098-2.662), and those lacking occupational health literacy (OR=2.562, 95%CI:2.004-3.276) had an increased risk of occupational stress. The Bayesian network model consisted of 9 nodes and 14 directed edges. Industry category, enterprise nature, weekly working hours, night shift, health status and occupational health literacy were direct influencing factors, while gender was an indirect influencing factor. The classification accuracy of the model was 77.12%, with an AUC of 0.823 (95%CI:0.80-0.85), indicating good predictive efficiency and model fitting performance. Backward reasoning identified 6 core risk nodes, and industry category was the most sensitive variable.

Conclusion

Industry category, enterprise nature, weekly working hours, night shift, health status and occupational health literacy can be taken as the intervention targets for the prevention and control of occupational stress.

Bayesian network model  /  Occupational stress  /  Binary logistic regression
姚茜, 杨敬源, 杨瑶, 庹芳旭, 徐龙, 余妙, 刘勇. 基于贝叶斯网络的毕节市重点行业劳动者职业紧张影响因素分析. 现代预防医学, 2026 , 53 (16) : 2934 -2940 . DOI: 10.20043/j.cnki.MPM.202601435
Qian YAO, Jing-yuan YANG, Yao YANG, Fang-xu TUO, Long XU, Miao YU, Yong LIU. Influencing factors of occupational stress among workers in key industries in Bijie City on Bayesian network[J]. Modern Preventive Medicine, 2026 , 53 (16) : 2934 -2940 . DOI: 10.20043/j.cnki.MPM.202601435
职业紧张是个体工作要求与自身资源、能力失衡引发的持续性身心应激反应,会损害劳动者健康[1]。过度职业紧张可对劳动者的身心健康和企业安全生产构成双重威胁[2-3],成为影响产业工人健康的重要公共卫生问题[4]。有研究表明,我国重点行业劳动者的职业紧张检出率在26.32%~38.32%之间,受多个因素独立及共同作用影响[5-7]。由于不同行业劳动环境、作业强度差异导致职业紧张影响因素较多且作用复杂[8],既往研究多主要聚焦单一因素,难以揭示多变量间的复杂作用关系及作用机制。贝叶斯网络模型是一种基于概率推理的图形化分析工具,可直观呈现变量之间的依赖关系,逐渐广泛应用于职业健康领域[9-10]。毕节市作为贵州重要工业基地,重点行业职业紧张相关研究匮乏,本研究旨在分析其职业紧张流行现状,借助贝叶斯网络解析关键影响因素及作用机制,为制定针对性区域职业心理健康干预措施提供科学依据。
采用现况调查设计,依据《2025年全国重点人群职业健康素养监测与干预技术方案》及毕节市职业病危害分布。采用分层随机整群抽样,以接害人数≥10人的企业为整群抽样单位,按大型(≥1 000人)、中型(300~<1 000人)、小型(<300人)企业分层[11],各层随机抽样。最终纳入第二产业3个行业11家企业,包括煤炭开采和洗选业、非金属矿物制品业、电力/热力/燃气及水生产和供应业;第三产业5个行业13家用人单位,涵盖各级医疗机构医务人员、中小学教职工、环卫、快递及外卖、出租汽车及网约车从业人员。采用随机数字法从接害岗位一线劳动者中进行个体抽样。大型企业抽100人,中型企业抽60人,小微企业抽40人,不足时将该企业一线人员全部纳入。共纳入调查对象1 986名。
纳入标准:①年龄≥16周岁;②从事生产制造或服务类作业的一线劳动者(含劳务派遣人员,不含行政管理人员);③意识清楚,能正常沟通、理解书面信息。
排除标准:①严重认知功能障碍、精神疾病或无法配合调查者;②近半年内遭遇重大心理创伤;③企业行政管理人员。本项目经毕节市疾病预防控制中心伦理委员会审批(〔2025〕毕疾控伦审字第5号)。所有研究对象均知情同意并自愿参与。
调查问卷采用《全国重点人群职业健康素养监测调查个人问卷》,由经系统培训的专业调查员实施面对面调查。通过全国重点人群职业健康素养监测调查信息系统生成二维码,调查对象使用手机或平板等移动终端扫描二维码,签署电子知情同意书后,以匿名方式进行问卷填写。调查内容包括:①人口社会学特征:性别、年龄、文化程度、婚姻状况、月均收入、户口性质;②职业相关特征:行业类别、企业性质、企业规模、工龄、周工作时长等;③职业健康素养;④职业紧张状况。
采用《职业紧张核心量表(CoreOccupationalStress,COSS)》进行测评,该量表包含社会支持、组织与回报、要求与付出、自主性四个维度,共17个条目,每个条目按照“1~5分”计分,除社会支持和自主性维度题目反向计分,各维度得分相加计算COSS总分,COSS总分≥50分判定为存在职业紧张[12]。该量表在本研究中Cronbach α系数为0.89。
调查前统一培训调查员,明确研究目的、问卷填写规范及质量控制要求,确保调查流程标准化;与被调查单位充分沟通,获得支持与配合,明确调查匿名性与保密性,提升应答率及填写真实性。调查过程中,通过现场指导、匿名自填、网络问卷逻辑校验等方式,减少填写误差,对回收问卷剔除逻辑矛盾、关键信息缺失或重复率高的无效问卷。
使用Excel 2021建立数据库并完成数据录入与整理,SPSS 26.0进行统计分析,计量资料以均数±标准差()描述;计数资料采用频数、构成比(%)表示,组间差异比较采用χ2检验。结合专业知识、既往文献及单因素分析中P<0.05的变量,纳入二元logistic回归模型(后退法,纳入水准0.05,剔除水准0.10)。采用GeNIe 4.0构建贝叶斯网络模型,敏感性分析识别关键影响因子;以受试者工作特征曲线下面积(AUC)评价模型拟合效果,AUC>0.7为拟合较好。逆向推理假定职业紧张检出率为100%,计算各节点概率变化幅度,将变化率>5%的节点判定为关键影响因素[13]。检验水准α=0.05(双侧)。
本次调查共计发放问卷1 986份,回收有效问卷1 871份,有效回收率为94.21%。其中男性1 286人(68.73%),女性585人(31.27%),男女性别比为2.20:1;平均年龄(38.30±9.45)岁,其中30~40岁年龄段占比最大(n=717,38.32%);婚姻以已婚为主(n=1 456,77.82%);文化程度以大学本科及以上最多(n=580,31.00%);实际平均月收入集中在5 000~6 999元(n=663,35.44%);户口性质以农业户口为主(n=1 035,55.32%)。见表1。
本研究共检出职业紧张劳动者559例,检出率为29.88%。由单因素分析得,性别、文化程度、行业类别、企业性质、周工作时长、夜班、健康状态、一年内因病请假休工情况、职业健康素养是职业紧张的影响因素,且差异具有统计学意义(P均<0.005),见表1。
变量赋值情况见表2。以职业紧张为因变量,单因素分析中差异有统计学意义的变量作为自变量,采用后退法(纳入水准0.05,剔除水准0.10)进行二元logistic回归分析,结果显示:性别、行业类别、企业性质、周工作时长、夜班、健康状态、一年内因病请假休工情况及职业健康素养是职业紧张发生的独立影响因素,其中女性、民营企业劳动者、上夜班者、自评健康状态为一般、不具备职业健康素养、煤炭开采和洗选业发生职业紧张风险较高。见表3。
基于二元logistic回归筛选的8个变量构建贝叶斯网络模型,结合表4变量赋值,通过结构学习明确各变量对职业紧张的直接与间接作用关系,参数学习获得各节点条件概率表以量化不同状态下职业紧张发生概率。结果显示,模型含9个网络节点、14条有向边,行业类别、企业性质、周工作时长、夜班、健康状态及职业健康素养对职业紧张有直接影响,性别为间接影响因素,见图1。模型整体分类准确率为77.12%,ROC曲线AUC值为0.823(95%CI:0.80~0.85)>0.70,说明模型预测性能较好,见图2。
逆向推理将职业紧张检出率设为100%,计算各节点变化率,结果显示6个节点变化率>5%,为职业紧张的风险节点,分别是企业性质、健康状态、行业类别、职业健康素养、夜班和周工作时长。以检出职业紧张为目标变量进行敏感性分析,节点的敏感度的高低用节点颜色的深浅来表示,按敏感度指数排序可得行业类别>周工作时长>健康状态=夜班=职业健康素养>企业性质。见图3和表5。
本研究显示,毕节市重点行业劳动者职业紧张检出率为29.88%,高于贵州省和江苏省既往报道水平[7,14],提示该地区职业紧张问题较为突出。二元logistic回归分析表明,性别、行业类型、企业性质、周工作时长、夜班、健康状况、因病休工情况及职业健康素养均为职业紧张的独立影响因素。其中女性、民营企业、上夜班、自评健康状态为一般、不具备职业健康素养、煤炭开采和洗选业劳动者更易检出职业紧张。
敏感性分析显示,行业类别为最敏感的影响因素,其中煤炭开采和洗选业劳动者职业紧张风险明显增加,这与毕节市产业结构特点高度契合。毕节市作为贵州省重要能源地,该行业作业环境复杂、劳动强度大、保障不足,易引发劳动者心理安全感缺失[15],与刘文亚等[16]研究结论一致。本研究发现周工作时长≤40 h者职业紧张风险增加,与既往研究存在差异[5,17],可能是研究群体多为快递/外卖配送,电力/热力/燃气及水生产和供应业等行业,虽工时合规但受灵活用工、工作自主性低、收入不稳定等因素影响,心理压力更突出。夜班从业者职业紧张风险更高,因夜班可扰乱生物节律,引发睡眠障碍与内分泌紊乱,且减少社会互动,削弱社会支持,导致压力无法有效疏导,从而增加职业紧张风险[18-19]。自评健康一般者职业紧张风险较高,与王丹[20]研究结论不一致,可能因健康较差者减少工作投入降低压力暴露,而健康一般者需承担高强度工作,身心缓冲能力不足。职业健康素养与职业紧张呈负相关,与既往研究一致[5,21],素养较低者因职业技能相对单一、防护意识薄弱,难以调节心理压力,长期处于需求与能力不匹配的困境,从而更易陷入职业紧张[5,22],而高学历者可通过提升素养增强岗位适配度[23],因此职业健康素养是职业紧张预防和干预的关键可改变因素。事业单位劳动者职业紧张风险低于民营企业,因中小型民营企业及其他类单位受规模限制,薪酬福利、健康管理体系不完善,导致职业紧张风险升高;而事业单位工作稳定性高、保障较为完善,风险相对较低。
本研究存在一定局限性:一是自我填报数据可能存在测量偏倚;二为横断面研究无法明确因果时序,贝叶斯网络仅体现变量关联;三是样本局限于毕节市重点行业,结论外推需谨慎。
综上所述,贝叶斯网络模型预测效能良好,行业类别、周工作时长、健康状况、夜班、职业健康素养、企业性质为职业紧张的关键影响因素。应重点关注煤炭开采洗选业等高风险行业,针对短工时、夜班、民营企业及不具备职业健康素养的劳动者,采取针对性措施有效防控职业紧张发生。
  • 毕节市科学技术局科学技术联合基金(毕科联合〔2025〕150号)
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2026年第53卷第16期
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doi: 10.20043/j.cnki.MPM.202601435
  • 接收时间:2026-01-27
  • 首发时间:2026-09-28
  • 出版时间:2026-08-25
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  • 收稿日期:2026-01-27
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毕节市科学技术局科学技术联合基金(毕科联合〔2025〕150号)
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    1.贵州医科大学公共卫生与健康学院环境污染与疾病监控教育部重点实验室,贵州 贵阳 561113
    2.毕节市疾病预防控制中心
    3.毕节医学高等专科学校

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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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