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Article(id=1325503411910738591, tenantId=1146029695717560320, journalId=1256213402557890569, issueId=1325502070660407522, articleNumber=null, orderNo=null, doi=10.16835/j.cnki.1000-9817.2025151, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1726156800000, receivedDateStr=2024-09-13, revisedDate=1737043200000, revisedDateStr=2025-01-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1793954240062, onlineDateStr=2026-11-06, pubDate=1748102400000, pubDateStr=2025-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793954240062, onlineIssueDateStr=2026-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793954240062, creator=13701087609, updateTime=1793954240062, updator=13701087609, issue=Issue{id=1325502070660407522, tenantId=1146029695717560320, journalId=1256213402557890569, year='2025', volume='46', issue='5', pageStart='609', pageEnd='760', issueExtLink='null', onlineDate='null', pubDate='1748102400000', pubDateStr='2025-05-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793953920283, creator='13701087609', updateTime=1793953920283, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=690, endPage=693, ext={EN=ArticleExt(id=1325503412737016480, articleId=1325503411910738591, tenantId=1146029695717560320, journalId=1256213402557890569, language=EN, title=Analysis of depressive symptoms and influencing factors among university students in Shandong Province, columnId=1259565855189418448, journalTitle=Chinese Journal of School Health, columnName=Mental Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the prevalence and influencing factors of depressive symptoms among university students in some universities in Shandong Province, so as to provide a theoretical reference for implementing corresponding intervention measures.

Methods

A cluster-sampling method was used to select 8 079 university students studying in universities in Shandong Province from March 2023 to May 2024 as the research subjects. They were randomly divided into a training set (6 463) and a validation set (1 616) according to the 8/2 ratio. The influencing factors of depression among university students were analyzed, and a risk prediction model for depression among university students was constructed and validated.

Results

In the training set of university students, the detection rate of depression was 35.09%(2 268/6 463), with 1 632 cases (71.96%) of mild depression, 545 cases (24.03%) of moderate depression, and 91 cases (4.01%) of severe depression. In the validation set of university students, the detection rate of depression was 33.97% (549/1 616), with 384 cases (69.95%) of mild depression, 127 cases (23.13%) of moderate depression, and 38 cases (6.92%) of severe depression. In the training set, the proportions of those who surfed the Internet for more than 3 h/d, occasionally or did not participate in physical exercise, had average or poor relationships with classmates, often drank sugary drinks, occasionally or did not have breakfast, had unsatisfactory academic performance, had an average monthly living expense of less than 1 500 yuan on campus, and had divorced or widowed parents in the depression-detected group were all higher than those in the undetected group(χ2=1 193.85, 1 584.41, 1 115.10, 826.00, 1 424.05, 924.58, 803.68, 797.65, P < 0.05). The scores of the Chinese version of the Childhood Trauma Questionnaire (CTQ-SF) in the depression-detected group were also higher than those in the undetected group(t=98.48, P < 0.05). The results of Logistic regression analysis showed that physical exercise, classmate relationships, academic performance, average monthly living expenses on campus, and CTQ-SF scores were influencing factors for depression among university students(OR=3.87, 4.82, 3.63, 3.75, 4.39, P < 0.05). The sensitivity of the model in the training set for predicting depression among university students was 89.0%(95%CI=87.6%-90.2%), the specificity was 93.0% (95%CI=92.2%-93.7%), and the area under the curve was 0.9(95%CI=0.8-1.0); the sensitivity of the model in the validation set for predicting depression among university students was 87.6%(95%CI=84.5%-90.1%), the specificity was 91.3%(95%CI=89.4%-92.9%), and the area under the curve was 0.9(95%CI=0.8-1.0).

Conclusions

The high detection rate of depressive symptoms among university students in some universities in Shandong Province warrants attention. Constructing a risk prediction model is helpful for early identifying the risk of depression among university students.

, authors=Chang GAO1, Yehao YAN2, Cuicui ZHANG*, 3, authorsList=Chang GAO, Yehao YAN, Cuicui ZHANG, authorCompany=null, correspAuthors=Cuicui ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2025 Chinese Journal of School Health. All rights reserved., 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=1325503414515401379, articleId=1325503411910738591, tenantId=1146029695717560320, journalId=1256213402557890569, language=CN, title=山东省大学生抑郁症状现状及影响因素分析, columnId=1259565858817491410, journalTitle=中国学校卫生, columnName=心理卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探讨山东省高校大学生抑郁症状现状及影响因素，为实施相应的干预措施提供参考依据。

方法

采用整群抽样法，选择2023年3月至2024年5月在山东省内高校就读的8 079名大学生为研究对象，以8/2定律随机分为训练集(6 463名)和验证集(1 616名)。分析大学生抑郁症状的影响因素，构建并验证大学生发生抑郁症状的风险预测模型。

结果

训练集大学生抑郁症状检出率为35.09%(2 268/6 463)，其中轻度抑郁症状1 632名(71.96%)、中度抑郁症状545名(24.03%)、重度抑郁症状91名(4.01%)；验证集高校大学生抑郁症状检出率为33.97%(549/1 616)，其中轻度抑郁症状384名(69.95%)、中度抑郁症状127名(23.13%)、重度抑郁症状38名(6.92%)。训练集中抑郁症状检出组每天上网时间>3 h、偶尔或不参加体育运动、同学关系为一般或不好、经常喝含糖饮料、偶尔吃或不吃早餐、学习成绩不理想、在校每月生活费 < 1 500元、父母婚姻状况为离异或丧偶占比均高于未检出组(χ2值分别为1 193.85，1 584.41，1 115.10，826.00，1 424.05，924.58，803.68，797.65)、中文版儿童期创伤问卷(CTQ-SF)评分亦高于未检出组(t=98.48)(P值均＜0.05)。Logistic回归分析结果显示，体育运动、同学关系、学习成绩、在校平均每月生活费、CTQ-SF评分为高校大学生发生抑郁症状的影响因素(OR值分别为3.87，4.82，3.63，3.75，4.39，P值均＜0.05)。训练集模型预测高校大学生检出抑郁症状的灵敏度为89.0%(95%CI=87.6%~90.2%)，特异度为93.0%(95%CI=92.2%~93.7%)，曲线下面积为0.9(95%CI=0.8~1.0)；验证集模型高校大学生发生抑郁症状的灵敏度为87.6%(95%CI=84.5%~90.1%)，特异度为91.3%(95%CI=89.4%~92.9%)，曲线下面积为0.9(95%CI=0.8~1.0)。

结论

山东省大学生抑郁症状检出率较高，构建风险预测模型有助于早期甄别大学生抑郁症状发生风险。

, authors=高昶1, 闫业浩2, 张翠翠*, 3, authorsList=高昶, 闫业浩, 张翠翠, authorCompany=null, correspAuthors=张翠翠, authorNote=

高昶(1983-)，男，山东汶上人，硕士，高级实验师，主要研究方向为公共卫生、行为医学与脑科学

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张翠翠，E-mail：
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Nurs Res, 2022, 36(9): 1529-1536. 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Comparing the general data of the occurrence group and the non-occurrence group with depressive symptoms in the training set

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因素检出组(n=2 268)未检出组(n=4 195)χ2值P值
性别
  男1 025(45.19)1 974(47.06)2.050.15
  女1 243(54.81)2 221(52.94)
民族
  汉族1 954(86.16)3 625(86.41)0.080.77
  其他314(13.84)570(13.59)
父亲受教育程度
  初中及以下712(31.39)1 651(39.36)40.25＜0.01
  高中及以上1 556(68.61)2 544(60.64)
母亲受教育程度
  初中及以下872(38.45)1 763(42.03)7.81＜0.01
  高中及以上1 396(61.55)2 432(57.97)
每天上网时间/h
  ≤3726(32.01)3 189(76.02)1 193.85＜0.01
  >31 542(67.99)1 006(23.98)
体育运动
  经常参加765(33.73)3 481(82.98)1 584.41＜0.01
  偶尔或不参加1 503(66.27)714(17.02)
同学关系
  良好812(35.80)3 264(77.81)1 115.10＜0.01
  一般/不好1 456(64.20)931(22.19)
早餐食用情况
  经常吃751(33.11)3 372(80.39)1 424.05＜0.01
  偶尔吃或不吃1 517(66.89)823(19.62)
含糖饮料
  经常喝1 375(60.63)1 025(24.43)826.00＜0.01
  不经常喝893(39.37)3 170(78.57)
学习成绩
  理想752(33.16)3 029(72.21)924.58＜0.01
  不理想1 516(66.84)1 166(27.79)
在校平均月生活费/元
  ≥1 500789(34.79)2 987(71.20)803.68＜0.01
  ＜1 5001 479(65.21)1 208(28.80)
父母婚姻状况
  正常1 376(60.67)3 784(90.20)797.65＜0.01
  离异/丧偶892(39.33)411(9.80)
), ArticleFig(id=1325503417875038919, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325503411910738591, language=CN, label=表1, caption=

训练集中抑郁症状检出组与未检出组被试一般资料分布比较

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因素检出组(n=2 268)未检出组(n=4 195)χ2值P值
性别
  男1 025(45.19)1 974(47.06)2.050.15
  女1 243(54.81)2 221(52.94)
民族
  汉族1 954(86.16)3 625(86.41)0.080.77
  其他314(13.84)570(13.59)
父亲受教育程度
  初中及以下712(31.39)1 651(39.36)40.25＜0.01
  高中及以上1 556(68.61)2 544(60.64)
母亲受教育程度
  初中及以下872(38.45)1 763(42.03)7.81＜0.01
  高中及以上1 396(61.55)2 432(57.97)
每天上网时间/h
  ≤3726(32.01)3 189(76.02)1 193.85＜0.01
  >31 542(67.99)1 006(23.98)
体育运动
  经常参加765(33.73)3 481(82.98)1 584.41＜0.01
  偶尔或不参加1 503(66.27)714(17.02)
同学关系
  良好812(35.80)3 264(77.81)1 115.10＜0.01
  一般/不好1 456(64.20)931(22.19)
早餐食用情况
  经常吃751(33.11)3 372(80.39)1 424.05＜0.01
  偶尔吃或不吃1 517(66.89)823(19.62)
含糖饮料
  经常喝1 375(60.63)1 025(24.43)826.00＜0.01
  不经常喝893(39.37)3 170(78.57)
学习成绩
  理想752(33.16)3 029(72.21)924.58＜0.01
  不理想1 516(66.84)1 166(27.79)
在校平均月生活费/元
  ≥1 500789(34.79)2 987(71.20)803.68＜0.01
  ＜1 5001 479(65.21)1 208(28.80)
父母婚姻状况
  正常1 376(60.67)3 784(90.20)797.65＜0.01
  离异/丧偶892(39.33)411(9.80)
), ArticleFig(id=1325503417954730696, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325503411910738591, language=EN, label=Tab.2, caption=

Logistic regression analysis of the influencing factors of depressive symptoms among university students(n=6 463)

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因素β值标准误Wald χ2值OR值(95%CI)P值
体育运动1.350.4210.533.87(1.70~8.80)＜0.01
同学关系1.570.4711.444.82(2.12~10.97)＜0.01
学习成绩1.290.516.423.63(1.60~8.26)＜0.01
在校平均月生活费1.320.449.103.75(1.65~8.52)＜0.01
CTQ-SF评分1.480.3715.814.39(1.93~9.98)＜0.01
), ArticleFig(id=1325503418562904777, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325503411910738591, language=CN, label=表2, caption=

高校大学生抑郁症状影响因素Logistic回归分析(n=6 463)

, figureFileSmall=null, figureFileBig=null, tableContent=
因素β值标准误Wald χ2值OR值(95%CI)P值
体育运动1.350.4210.533.87(1.70~8.80)＜0.01
同学关系1.570.4711.444.82(2.12~10.97)＜0.01
学习成绩1.290.516.423.63(1.60~8.26)＜0.01
在校平均月生活费1.320.449.103.75(1.65~8.52)＜0.01
CTQ-SF评分1.480.3715.814.39(1.93~9.98)＜0.01
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山东省大学生抑郁症状现状及影响因素分析
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高昶 1 , 闫业浩 2 , 张翠翠 *, 3
中国学校卫生 | 心理卫生 2025,46(5): 690-693
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中国学校卫生 |心理卫生 2025 , 46 (5) : 690 -693
山东省大学生抑郁症状现状及影响因素分析
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高昶1, 闫业浩2, 张翠翠*, 3
作者信息
  • 1济宁医学院精神卫生学院行为医学重点实验室，山东 272067
  • 2济宁医学院公共卫生学院
  • 3济宁医学院附属医院重症医学三科
通讯作者:
张翠翠，E-mail：
作者简介:

高昶(1983-)，男，山东汶上人，硕士，高级实验师，主要研究方向为公共卫生、行为医学与脑科学

Analysis of depressive symptoms and influencing factors among university students in Shandong Province
Chang GAO1, Yehao YAN2, Cuicui ZHANG*, 3
Affiliations
  • 1Key Laboratory of Behavioral Medicine, School of Mental Health, Jining Medical University, Jining (272067), Shandong Province, China
出版时间: 2025-05-25 doi: 10.16835/j.cnki.1000-9817.2025151
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摘要
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目的

探讨山东省高校大学生抑郁症状现状及影响因素，为实施相应的干预措施提供参考依据。

方法

采用整群抽样法，选择2023年3月至2024年5月在山东省内高校就读的8 079名大学生为研究对象，以8/2定律随机分为训练集(6 463名)和验证集(1 616名)。分析大学生抑郁症状的影响因素，构建并验证大学生发生抑郁症状的风险预测模型。

结果

训练集大学生抑郁症状检出率为35.09%(2 268/6 463)，其中轻度抑郁症状1 632名(71.96%)、中度抑郁症状545名(24.03%)、重度抑郁症状91名(4.01%)；验证集高校大学生抑郁症状检出率为33.97%(549/1 616)，其中轻度抑郁症状384名(69.95%)、中度抑郁症状127名(23.13%)、重度抑郁症状38名(6.92%)。训练集中抑郁症状检出组每天上网时间>3 h、偶尔或不参加体育运动、同学关系为一般或不好、经常喝含糖饮料、偶尔吃或不吃早餐、学习成绩不理想、在校每月生活费 < 1 500元、父母婚姻状况为离异或丧偶占比均高于未检出组(χ2值分别为1 193.85，1 584.41，1 115.10，826.00，1 424.05，924.58，803.68，797.65)、中文版儿童期创伤问卷(CTQ-SF)评分亦高于未检出组(t=98.48)(P值均＜0.05)。Logistic回归分析结果显示，体育运动、同学关系、学习成绩、在校平均每月生活费、CTQ-SF评分为高校大学生发生抑郁症状的影响因素(OR值分别为3.87，4.82，3.63，3.75，4.39，P值均＜0.05)。训练集模型预测高校大学生检出抑郁症状的灵敏度为89.0%(95%CI=87.6%~90.2%)，特异度为93.0%(95%CI=92.2%~93.7%)，曲线下面积为0.9(95%CI=0.8~1.0)；验证集模型高校大学生发生抑郁症状的灵敏度为87.6%(95%CI=84.5%~90.1%)，特异度为91.3%(95%CI=89.4%~92.9%)，曲线下面积为0.9(95%CI=0.8~1.0)。

结论

山东省大学生抑郁症状检出率较高，构建风险预测模型有助于早期甄别大学生抑郁症状发生风险。

抑郁  /  精神卫生  /  回归分析  /  ROC曲线  /  学生
Objective

To explore the prevalence and influencing factors of depressive symptoms among university students in some universities in Shandong Province, so as to provide a theoretical reference for implementing corresponding intervention measures.

Methods

A cluster-sampling method was used to select 8 079 university students studying in universities in Shandong Province from March 2023 to May 2024 as the research subjects. They were randomly divided into a training set (6 463) and a validation set (1 616) according to the 8/2 ratio. The influencing factors of depression among university students were analyzed, and a risk prediction model for depression among university students was constructed and validated.

Results

In the training set of university students, the detection rate of depression was 35.09%(2 268/6 463), with 1 632 cases (71.96%) of mild depression, 545 cases (24.03%) of moderate depression, and 91 cases (4.01%) of severe depression. In the validation set of university students, the detection rate of depression was 33.97% (549/1 616), with 384 cases (69.95%) of mild depression, 127 cases (23.13%) of moderate depression, and 38 cases (6.92%) of severe depression. In the training set, the proportions of those who surfed the Internet for more than 3 h/d, occasionally or did not participate in physical exercise, had average or poor relationships with classmates, often drank sugary drinks, occasionally or did not have breakfast, had unsatisfactory academic performance, had an average monthly living expense of less than 1 500 yuan on campus, and had divorced or widowed parents in the depression-detected group were all higher than those in the undetected group(χ2=1 193.85, 1 584.41, 1 115.10, 826.00, 1 424.05, 924.58, 803.68, 797.65, P < 0.05). The scores of the Chinese version of the Childhood Trauma Questionnaire (CTQ-SF) in the depression-detected group were also higher than those in the undetected group(t=98.48, P < 0.05). The results of Logistic regression analysis showed that physical exercise, classmate relationships, academic performance, average monthly living expenses on campus, and CTQ-SF scores were influencing factors for depression among university students(OR=3.87, 4.82, 3.63, 3.75, 4.39, P < 0.05). The sensitivity of the model in the training set for predicting depression among university students was 89.0%(95%CI=87.6%-90.2%), the specificity was 93.0% (95%CI=92.2%-93.7%), and the area under the curve was 0.9(95%CI=0.8-1.0); the sensitivity of the model in the validation set for predicting depression among university students was 87.6%(95%CI=84.5%-90.1%), the specificity was 91.3%(95%CI=89.4%-92.9%), and the area under the curve was 0.9(95%CI=0.8-1.0).

Conclusions

The high detection rate of depressive symptoms among university students in some universities in Shandong Province warrants attention. Constructing a risk prediction model is helpful for early identifying the risk of depression among university students.

Depression  /  Mental health  /  Regression analysis  /  ROC curve  /  Students
高昶, 闫业浩, 张翠翠. 山东省大学生抑郁症状现状及影响因素分析. 中国学校卫生, 2025 , 46 (5) : 690 -693 . DOI: 10.16835/j.cnki.1000-9817.2025151
Chang GAO, Yehao YAN, Cuicui ZHANG. Analysis of depressive symptoms and influencing factors among university students in Shandong Province[J]. Chinese Journal of School Health, 2025 , 46 (5) : 690 -693 . DOI: 10.16835/j.cnki.1000-9817.2025151
正文
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抑郁症状是一种常见且严重的精神疾病，主要特征是持续的情绪低落、兴趣减退和快感缺失，会显著影响患者的日常生活和功能[1]。近年来，抑郁症状的发病率呈逐年升高趋势，世界卫生组织提出抑郁症状为全球过早死亡及残疾的重要因素之一[2]。中国大学生抑郁症状的总体平均发病率高达23.8%[3]。性别、环境因素、家庭因素、遗传因素等与大学生抑郁症状的发生关系密切[4]。长期的抑郁状态会削弱学生的社会适应能力和学习效率，进一步影响学业和生活质量，许多抑郁症状患者还会出现自杀想法或企图，甚至实施自杀行为[5-6]。本研究于2023年3月至2024年5月调查山东省部分高校大学生抑郁症状现状及其影响因素，并构建列线图风险模型，以期为降低大学生抑郁症状发生风险提供一定的科学依据。
1　对象与方法
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1.1　对象
于2023年3月至2024年5月，采用整群抽样方法在山东省选择3所高校(济宁学院、曲阜师范大学、济宁医学院)，在校内通过二维码或年级群对在读大学生进行问卷调查。采用直接抽选法选取641个班级，8 164名大学生作为调查对象。因3道质量控制题目均未答对剔除85份调查问卷，最终回收8 079份有效问卷，有效回收率为98.96%。其中女生4 295名，男生3 784名；年龄17~24岁，平均(21.08±1.45)岁。以8/2定律随机分为训练集(6 463名)和验证集(1 616名)。本研究所有研究对象均知情同意，获得济宁医学院医学伦理委员会许可(编号：JNMC-YX-2025-057)。
1.2　方法
1.2.1　抑郁症状评估
通过抑郁自评量表(Self-rating Depression Scale，SDS)[7]对高校大学生最近一周的抑郁症状进行评估。量表包括20个项目，每个项目1~4分，行为或感觉频率持续、经常、有时、无分别记为4，3，2，1分，总分为20~80分，原始总分×1.25为标准分数，标准分值＜53，53~＜62，62~72，≥73分分别记为无、轻度、中度、重度抑郁症状。该量表具有良好的信、效度，Cronbach α系数为0.88。
1.2.2　问卷调查
自制问卷内容包括性别、民族、父母受教育程度、家庭居住地、每天上网时间、体育运动(经常参加：每周运动≥2次；偶尔或不参加：每周运动≤1次)、同学关系、早餐食用情况(经常吃：每周≥3次；偶尔吃或不吃：每周0~2次)、含糖饮料(经常喝：每周喝含糖饮料≥3次，不经常喝：每周喝含糖饮料＜3次)、学习成绩(理想：所有科目平均分≥80分；不理想：所有科目平均分＜80分)、在校平均月生活费、年级、父母婚姻状况等。
1.2.3　中文版儿童期创伤问卷(Childhood Trauma Questionnaire-Short Form，CTQ-SF)[8]
包括躯体虐待、情感忽视、躯体忽视、情感虐待、性虐待5个维度，每个维度包括5个条目，每个条目按照发生频度分为总是、经常、有时、偶尔、从不，分别记为5，4，3，2，1分，总分25~125分，分值越低代表受虐程度越轻，本研究中以40分为临界值，分为高(≥40分)、低(＜4分)评分，且≥40分的大学生儿童期创伤为严重。量表总Cronbach α系数为0.73。
1.3　质量控制
为保障研究结果的准确性及有效性，调查前向所有高校大学生发放告知书，介绍本次调查的方法、意义与目的，确保每名大学生知情同意。在正式调研前，通过专家咨询和预调查对电子问卷内容与条目进行完善、修改与制定。调查前，在3所高校招募联络员，培训后明确研究的意义与目的。通过联络员严格控制校内发放问卷链接与二维码，确保回答者信息的真实性。所有参与者自愿且匿名在电脑或智能设备上完成电子问卷，填写结果只允许提交一次。为保障数据质量，在问卷中随机加入3道质量控制题目，3道题目均答对的问卷即为有效问卷，调查后录入数据，由2人复核调查问卷。
1.4　统计学分析
使用SPSS 23.0软件对数据进行分析。通过频数或百分比描述计数资料，组间比较使用χ2检验。计量资料经正态性检验符合正态分布用(x ±s)表示，组间比较使用t检验。通过Logistic回归模型分析大学生抑郁症状发生的影响因素；建立列线图风险模型，各危险因素(体育运动、同学关系、学习成绩、在校月生活费)分析中b值最大的因素赋值为100分，其余因素根据其b值与最大b值占比记分；采用Bootstrap法进行验证。以受试者工作特性曲线(ROC)评价预测价值。以P＜0.05为有统计学意义。
2　结果
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2.1　大学生抑郁症状现状
训练集6 463名高校大学生抑郁症状检出率为35.09%(2 268名)，其中轻度抑郁症状1 632名(71.96%)、中度抑郁症状545名(24.03%)、重度抑郁症状91名(4.01%)；验证集1 616名高校大学生中，抑郁症状检出率为33.97%(549名)，其中轻度抑郁症状384名(69.95%)、中度抑郁症状127名(23.13%)、重度抑郁症状38名(6.92%)。
2.2　训练集中抑郁症状检出组与未检出组被试的一般资料比较
训练集中每天上网时间、体育运动、同学关系、含糖饮料、早餐食用情况、学习成绩、在校平均月生活费、父母婚姻状况分布在是否检出抑郁症状被试间差异均有统计学意义(P值均＜0.05)。抑郁症状被试的CTQ-SF评分(48.52±5.67)高于未检出抑郁症状的被试(36.15±4.29)(t=98.48，P＜0.05)。见表1。
2.3　大学生抑郁症状的影响因素
以大学生是否发生抑郁症状为因变量(是=1，否=0)，以每天上网时间(>3 h=1，≤3 h=0)、体育运动(经常参加=0，偶尔或不参加=1)、同学关系(一般或不好=1，良好=0)、含糖饮料(经常喝=1，不经常喝=0)、学习成绩(理想=0，不理想=1)、早餐食用情况(偶尔吃或不吃=1，经常吃=0)、在校月生活费(＜1 500元=1，≥1 500元=0)、父母婚姻状况(离异或丧偶=1，正常=0)、CTQ-SF评分[高(得分≥40分)=1，低(得分＜40分)=0]为自变量进行Logistic回归分析，结果显示，体育运动、同学关系、学习成绩、在校平均每月生活费、CTQ-SF评分为高校大学生发生抑郁症状的影响因素(P值均＜0.05)。见表2。
2.4　大学生抑郁症状列线图预测模型建立
以高校大学生抑郁症状的影响因素为变量构建列线图模型。结果如下：体育运动为86分，同学关系为100分，学习成绩为82分，在校平均月生活费84分，CTQ-SF评分为94分，得分值越高提示高校大学生发生抑郁症状的风险越大。见图1。
2.5　列线图预测模型验证
Bootstrap法结果显示，C-index指数为0.9(95%CI=0.7~0.9)，具有良好的区分度。Calibration曲线结果显示，理想曲线与校正曲线具有良好的拟合度。Hosmer-Lemeshow检验结果显示，2种曲线差异无统计学意义(χ2=0.44，P=0.48)。以高校大学生发生抑郁症状为因变量，风险总分为自变量，训练集模型预测高校大学生发生抑郁症状的灵敏度为89.0%(95%CI=87.6%~90.2%)，特异度为93.0%(95%CI=92.2%~93.7%)，曲线下面积为0.9(95%CI=0.8~1.0)；验证集模型高校大学生发生抑郁症状的灵敏度为87.6%(95%CI=84.5%~90.1%)，特异度为91.3%(95%CI=89.4%~92.9%)，曲线下面积为0.9(95%CI=0.8~1.0)。
3　讨论
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大学生在成长过程中面临许多心理挑战，例如自我认同的探索、理想与现实的矛盾、情感问题以及人际关系的压力等，可能导致焦虑和抑郁情绪的产生。
本研究中，训练集6 463名高校大学生抑郁症状检出率较高(35.09%)，应引起重视。孙景芬等[9]研究结果显示，沈阳市两所高校大学生抑郁症状发生率为37.00%，且以轻度抑郁为主，与本研究结果相符。本研究中，体育运动、同学关系、学习成绩、在校月生活费、CTQ-SF评分为高校大学生发生抑郁症状的影响因素。从生理机制来看，体育运动可以显著影响大脑中的神经递质分泌，如运动能够促进多巴胺、血清素和内啡肽等神经递质的释放，以上神经递质在调节情绪和压力反应中起着关键作用。多巴胺是一种与愉悦感相关的神经递质，而血清素对情绪稳定有重要作用。内啡肽不仅能够缓解痛苦，还能减轻压力和焦虑。此外，运动还可以增加脑血流量和肾上腺髓质分泌儿茶酚胺的能力，从而缓解抑郁症状。从心理健康的角度来看，体育运动有助于提高自信心、增强自我效能感以及改善认知功能[10]。同学之间的冲突和不良互动是导致抑郁症状的重要因素之一，经常性的冲突会增加患病概率。此外，长期处于负面情绪积累的状态下，也容易引发抑郁症状。在校园环境中，如果学生不能很好地处理与同学、老师的关系，可能会感到孤独和被排斥，这也可能是抑郁症状的诱因[11-12]。学习成绩与大学生的心理健康密切相关，研究表明，学业成绩不佳的学生更容易出现抑郁症状[13]。学习压力是导致抑郁症状的一个重要因素，尤其是在面对重大考试和升学压力时，学生可能会感到巨大的心理负担。此外，低学习成绩可能导致自尊心受损，进一步加剧抑郁情绪[14]。大学生的生活费用直接影响心理健康状况，经济困难的学生往往面临更多物质和精神上的压力，将显著降低心理健康水平[15]。和红等[16]研究结果显示，在校月生活费低、学习成绩不理想的大学生发生抑郁症状的风险更高，与本研究结果相符。CTQ-SF评分用于评估个体童年时期的创伤经历，有童年创伤经历的大学生在儿童期形成意义模糊的概念并“积存”起来，长大后会激活这些概念并赋予新的意义，通常会归咎于自责自罪及负性行为，从而增加抑郁症状发生风险[17]。侯佳璐等[18]研究指出，童年创伤为青少年抑郁症状发生的独立危险因素，与本研究报道相符。风险模型能够直观反映条件与事件发生的关系及影响程度，本研究基于山东省内部分高校大学生发生抑郁症状的影响因素构建风险预测模型，结果显示，该预测模型评估山东省内部分高校大学生抑郁症状发生风险具有一定价值。
综上所述，山东省高校大学生抑郁症状检出率较高，体育运动、同学关系、学习成绩、在校月生活费、CTQ-SF评分与大学生抑郁症状发生有关，构建风险预测模型有助于早期甄别高校大学生抑郁症状发生风险。但鉴于本研究选择样本量有限, 且只探讨山东省部分高校, 无法突出山东省高校整体大学生抑郁症状现状及影响因素, 后续可扩大样本量予以补充。
基金
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  • 济宁市重点研发计划项目(2019SMNS001)
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2025年第46卷第5期
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doi: 10.16835/j.cnki.1000-9817.2025151
  • 接收时间：2024-09-13
  • 首发时间：2026-11-06
  • 出版时间：2025-05-25
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  • 收稿日期：2024-09-13
  • 修回日期：2025-01-17
基金
济宁市重点研发计划项目(2019SMNS001)
作者信息
    1济宁医学院精神卫生学院行为医学重点实验室，山东 272067
    2济宁医学院公共卫生学院
    3济宁医学院附属医院重症医学三科

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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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