Article(id=1241035811756503687, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202405441, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1716480000000, receivedDateStr=2024-05-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815594853, onlineDateStr=2026-03-18, pubDate=1733760000000, pubDateStr=2024-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815594853, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815594853, creator=13701087609, updateTime=1773815594853, updator=13701087609, issue=Issue{id=1241035810628235909, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='23', pageStart='4225', pageEnd='4416', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815594584, creator=13701087609, updateTime=1773815743629, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241036435843764756, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241036435843764757, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4333, endPage=4337, ext={EN=ArticleExt(id=1241035812624724618, articleId=1241035811756503687, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of potential transitions in depression among college students, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the categories of depression among college students and their influencing factors.

Methods

A cluster convenience sampling method was employed, utilizing a depression scale and an online social exclusion questionnaire to conduct a longitudinal study over four months at two time points, involving 2 000 college students from seven universities across Jiangsu, Henan, Fujian, Gansu, Liaoning, Heilongjiang provinces, and the Inner Mongolia Autonomous Region. Latent transition analysis was used to investigate the latent categories of depression among college students and the probabilities of transitions between these categories.

Results

The overall incidence of depression among college students showed a declining trend. College students’ depression can be classified into two latent categories: low depression symptom group and high depression symptom group. The high depression symptom group exhibited the strongest stability, with a probability of maintaining the original latent state at 92.4%. The probability of transition from the low depression symptom group to the high depression symptom group was 52.7%, while the probability of transition from the high depression symptom group to the low depression symptom group was 7.6%. Gender (OR=0.73, 95%CI: 0.57-0.93), age (OR=0.88, 95%CI: 0.79-0.98), and online social exclusion (OR=1.94, 95%CI: 1.56-2.41) were identified as significant influencing factors in college students’ depression and its category transitions.

Conclusion

There are two latent categories of depression among college students, which change over time and are influenced by various factors.

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目的

探讨大学生抑郁的类别转变及其影响因素。

方法

采用整群方便抽样法,用抑郁量表和网络社会排斥问卷对江苏省、河南省、福建省、甘肃省、辽宁省、黑龙江省及内蒙古自治区7省共7所本科院校的2 000名大学生进行为期4个月共2个时间点的追踪调查,采用潜在转变分析探究大学生抑郁的潜在类别及转变概率。

结果

大学生整体的抑郁发生率呈下降趋势;大学生抑郁可分为低抑郁症状组和高抑郁症状组2个潜在类别;高抑郁症状组稳定性最强,保持原潜在状态的概率为92.4%,低抑郁症状组向高抑郁症状组转变的概率为52.7%,高抑郁症状组向低抑郁症状组转变的概率为7.6%;性别(OR=0.73,95%CI:0.57~0.93)、年龄(OR=0.88,95%CI:0.79~0.98)和网络社会排斥(OR= 1.94,95%CI:1.56~2.41)是大学生抑郁及其类别转变中的重要影响因素。

结论

大学生抑郁存在2个潜在类别,不同类别随时间推移发生转变且受多种因素影响。

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金童林,E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点,除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=8aSuOhBy8WJwRHl1u6s8iA==, magXml=KpAeIewky9K6Sx6+VWa21w==, pdfUrl=null, pdf=syzDcEU4AasjVkIJcQneiw==, pdfFileSize=689278, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=i/vAcPy0yj99SHIQvr8GgA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=J4ukSb4tEVRvR7hbsOUTzA==, mapNumber=null, authorCompany=null, fund=null, authors=

代海云(1998-),女,硕士在读,研究方向:发展与教育心理学

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Journal of Guizhou Normal University(Natural Sciences): 1-6 [2024-10-25]. http://kns.cnki.net/kcms/detail/52.5006.N.20240516.1708.046.html.(In Chinese), articleTitle=Influence of gratitude on depression of college freshmen: The chain mediating effects of the meaning in life and hope, refAbstract=null)], funds=[Fund(id=1241069122201637134, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, awardId=23BSH142, language=CN, fundingSource=国家社会科学基金一般项目(23BSH142), fundOrder=null, country=null), Fund(id=1241069122293911828, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, awardId=2022MS03004, language=CN, fundingSource=内蒙古自然科学基金面上项目(2022MS03004), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241069118791667717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, xref=1., ext=[AuthorCompanyExt(id=1241069118800056327, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, companyId=1241069118791667717, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Psychology, Inner Mongolia Normal University, Hohhot, Inner Mongolia 010022, China), AuthorCompanyExt(id=1241069118808444936, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, companyId=1241069118791667717, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.内蒙古师范大学心理学院,内蒙古 呼和浩特 010022)]), AuthorCompany(id=1241069118917496851, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, xref=2., ext=[AuthorCompanyExt(id=1241069118930079764, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, companyId=1241069118917496851, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.内蒙古高校人文社科重点研究基地心理健康教育研究与服务基地,内蒙古 呼和浩特 010022)])], figs=[ArticleFig(id=1241069120607801502, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=EN, label=Figure 1, caption=LCA classification results of depression in college students at T1 time point, figureFileSmall=vBMHC4DgtRfPKyIyQNtv/A==, figureFileBig=9FHixII2Gt9CVF3jFV9tcw==, tableContent=null), ArticleFig(id=1241069120746213541, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=CN, label=图1, caption=T1时间点大学生抑郁的LCA分类结果, figureFileSmall=vBMHC4DgtRfPKyIyQNtv/A==, figureFileBig=9FHixII2Gt9CVF3jFV9tcw==, tableContent=null), ArticleFig(id=1241069120922374326, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=EN, label=Figure 2, caption=LCA classification results of depression in college students at T2 time point, figureFileSmall=/dMmROb/YuvBjgYige8X7g==, figureFileBig=dZmcshzqO3FtnK0x/OQOiA==, tableContent=null), ArticleFig(id=1241069121073369280, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=CN, label=图2, caption=T2时间点大学生抑郁的LCA分类结果, figureFileSmall=/dMmROb/YuvBjgYige8X7g==, figureFileBig=dZmcshzqO3FtnK0x/OQOiA==, tableContent=null), ArticleFig(id=1241069121178226888, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=EN, label=Table 1, caption=

The incidence of depression in college students

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外显变量T1发生率(%)T2发生率(%)χ2P
W1我好像没有一点正面情绪12.18.71.6800.195
W2我感到很难激发做事情的主动性33.520.913.984<0.001
W3我觉得没有什么事情是可以期盼的13.611.52.0190.155
W4我感到情绪低落忧郁沮丧33.719.613.507<0.001
W5我无法热衷于任何事情16.113.35.6160.018
W6我觉得自己不怎么配做人7.77.92.2460.134
W7我觉得生活是没有意义的11.510.78.1160.004
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大学生抑郁的发生率

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外显变量T1发生率(%)T2发生率(%)χ2P
W1我好像没有一点正面情绪12.18.71.6800.195
W2我感到很难激发做事情的主动性33.520.913.984<0.001
W3我觉得没有什么事情是可以期盼的13.611.52.0190.155
W4我感到情绪低落忧郁沮丧33.719.613.507<0.001
W5我无法热衷于任何事情16.113.35.6160.018
W6我觉得自己不怎么配做人7.77.92.2460.134
W7我觉得生活是没有意义的11.510.78.1160.004
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The fitting index value of the latent class model with different class quantity at the time point T1 and T2

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时间点类别数目dfAICBICaBICEntropy LMR(P)BLRT(P)
1710 948.1110 986.9910 946.75
T12158 962.689 044.558 996.900.87<0.001<0.001
3238 756.028 881.568 808.490.80<0.001<0.001
4318 692.228 861.228 762.740.78<0.001<0.001
5398 687.018 899.878 775.970.790.7060.050
177 890.657 927.737 905.49
T22155 899.295 978.755 931.100.91<0.001<0.001
3235 718.855 840.685 767.620.83<0.001<0.001
4315 688.635 825.845 754.360.850.352<0.001
5395 670.535 877.115 753.220.840.476<0.001
), ArticleFig(id=1241069121543131361, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=CN, label=表2, caption=

T1、T2时间点上不同类别数量潜类别模型的拟合指标值

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时间点类别数目dfAICBICaBICEntropy LMR(P)BLRT(P)
1710 948.1110 986.9910 946.75
T12158 962.689 044.558 996.900.87<0.001<0.001
3238 756.028 881.568 808.490.80<0.001<0.001
4318 692.228 861.228 762.740.78<0.001<0.001
5398 687.018 899.878 775.970.790.7060.050
177 890.657 927.737 905.49
T22155 899.295 978.755 931.100.91<0.001<0.001
3235 718.855 840.685 767.620.83<0.001<0.001
4315 688.635 825.845 754.360.850.352<0.001
5395 670.535 877.115 753.220.840.476<0.001
), ArticleFig(id=1241069121685737706, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=EN, label=Table 3, caption=

T1 Potential state probability and regression table under the influence of covariates

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影响因素βWaldPOR值(95%CI)
留守经历(0=无, 1=有)-0.070.1570.1700.6800.94(0.69~1.28)
性别(0=女生, 1=男生)-0.320.1256.5340.0110.73(0.57~0.93)
年级(1=大一, 2=大二, 3=大三, 4=大四)0.020.0780.0880.7671.02(0.88~1.19)
专业(1=文科, 2=理科, 3=工科, 4=其他)0.140.2320.3390.5611.14(0.73~1.80)
年龄-0.130.0565.4010.0200.88(0.79~0.98)
网络社会排斥0.660.11135.747<0.0011.94(1.56~2.41)
), ArticleFig(id=1241069121849315570, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=CN, label=表3, caption=

协变量影响下T1潜在状态概率及回归表

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影响因素βWaldPOR值(95%CI)
留守经历(0=无, 1=有)-0.070.1570.1700.6800.94(0.69~1.28)
性别(0=女生, 1=男生)-0.320.1256.5340.0110.73(0.57~0.93)
年级(1=大一, 2=大二, 3=大三, 4=大四)0.020.0780.0880.7671.02(0.88~1.19)
专业(1=文科, 2=理科, 3=工科, 4=其他)0.140.2320.3390.5611.14(0.73~1.80)
年龄-0.130.0565.4010.0200.88(0.79~0.98)
网络社会排斥0.660.11135.747<0.0011.94(1.56~2.41)
), ArticleFig(id=1241069121949978874, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035811756503687, language=EN, label=Table 4, caption=

Odds ratio of transition probability under covariate influence

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影响因素类别转变OR值(95%CI)P
留守经历(0=无, 1=有)低抑郁症状组→高抑郁症状组1.01(0.67~1.52)0.973
高抑郁症状组→低抑郁症状组0.60(0.26~ 0.40)0.236
性别(0=女生, 1=男生)低抑郁症状组→高抑郁症状组0.84(0.60~1.17)0.297
高抑郁症状组→低抑郁症状组0.35(0.17~0.75)0.007
年龄低抑郁症状组→高抑郁症状组0.88(0.78~0.99)0.037
高抑郁症状组→低抑郁症状组0.87(0.68~1.12)0.283
网络社会排斥低抑郁症状组→高抑郁症状组1.74(1.31~2.30)<0.01
高抑郁症状组→低抑郁症状组1.22(0.63~2.39)0.557
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协变量影响下转变概率的优势比

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因素类别转变OR值(95%CI)P
留守经历(0=无, 1=有)低抑郁症状组→高抑郁症状组1.01(0.67~1.52)0.973
高抑郁症状组→低抑郁症状组0.60(0.26~ 0.40)0.236
性别(0=女生, 1=男生)低抑郁症状组→高抑郁症状组0.84(0.60~1.17)0.297
高抑郁症状组→低抑郁症状组0.35(0.17~0.75)0.007
年龄低抑郁症状组→高抑郁症状组0.88(0.78~0.99)0.037
高抑郁症状组→低抑郁症状组0.87(0.68~1.12)0.283
网络社会排斥低抑郁症状组→高抑郁症状组1.74(1.31~2.30)<0.01
高抑郁症状组→低抑郁症状组1.22(0.63~2.39)0.557
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大学生抑郁的潜在转变分析
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代海云 1 , 金童林 1, 2
现代预防医学 | 健康与社会行为 2024,51(23): 4333-4337
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现代预防医学 | 健康与社会行为 2024, 51(23): 4333-4337
大学生抑郁的潜在转变分析
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代海云1, 金童林1, 2
作者信息
  • 1.内蒙古师范大学心理学院,内蒙古 呼和浩特 010022
  • 2.内蒙古高校人文社科重点研究基地心理健康教育研究与服务基地,内蒙古 呼和浩特 010022
  • 代海云(1998-),女,硕士在读,研究方向:发展与教育心理学

通讯作者:

金童林,E-mail:
Analysis of potential transitions in depression among college students
Hai-yun DAI1, Tong-lin JIN1, 2
Affiliations
  • School of Psychology, Inner Mongolia Normal University, Hohhot, Inner Mongolia 010022, China
出版时间: 2024-12-10 doi: 10.20043/j.cnki.MPM.202405441
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目的

探讨大学生抑郁的类别转变及其影响因素。

方法

采用整群方便抽样法,用抑郁量表和网络社会排斥问卷对江苏省、河南省、福建省、甘肃省、辽宁省、黑龙江省及内蒙古自治区7省共7所本科院校的2 000名大学生进行为期4个月共2个时间点的追踪调查,采用潜在转变分析探究大学生抑郁的潜在类别及转变概率。

结果

大学生整体的抑郁发生率呈下降趋势;大学生抑郁可分为低抑郁症状组和高抑郁症状组2个潜在类别;高抑郁症状组稳定性最强,保持原潜在状态的概率为92.4%,低抑郁症状组向高抑郁症状组转变的概率为52.7%,高抑郁症状组向低抑郁症状组转变的概率为7.6%;性别(OR=0.73,95%CI:0.57~0.93)、年龄(OR=0.88,95%CI:0.79~0.98)和网络社会排斥(OR= 1.94,95%CI:1.56~2.41)是大学生抑郁及其类别转变中的重要影响因素。

结论

大学生抑郁存在2个潜在类别,不同类别随时间推移发生转变且受多种因素影响。

大学生  /  抑郁  /  潜在转变分析
Objective

To explore the categories of depression among college students and their influencing factors.

Methods

A cluster convenience sampling method was employed, utilizing a depression scale and an online social exclusion questionnaire to conduct a longitudinal study over four months at two time points, involving 2 000 college students from seven universities across Jiangsu, Henan, Fujian, Gansu, Liaoning, Heilongjiang provinces, and the Inner Mongolia Autonomous Region. Latent transition analysis was used to investigate the latent categories of depression among college students and the probabilities of transitions between these categories.

Results

The overall incidence of depression among college students showed a declining trend. College students’ depression can be classified into two latent categories: low depression symptom group and high depression symptom group. The high depression symptom group exhibited the strongest stability, with a probability of maintaining the original latent state at 92.4%. The probability of transition from the low depression symptom group to the high depression symptom group was 52.7%, while the probability of transition from the high depression symptom group to the low depression symptom group was 7.6%. Gender (OR=0.73, 95%CI: 0.57-0.93), age (OR=0.88, 95%CI: 0.79-0.98), and online social exclusion (OR=1.94, 95%CI: 1.56-2.41) were identified as significant influencing factors in college students’ depression and its category transitions.

Conclusion

There are two latent categories of depression among college students, which change over time and are influenced by various factors.

College students  /  Depression  /  Latent transition analysis
代海云, 金童林. 大学生抑郁的潜在转变分析. 现代预防医学, 2024 , 51 (23) : 4333 -4337 . DOI: 10.20043/j.cnki.MPM.202405441
Hai-yun DAI, Tong-lin JIN. Analysis of potential transitions in depression among college students[J]. Modern Preventive Medicine, 2024 , 51 (23) : 4333 -4337 . DOI: 10.20043/j.cnki.MPM.202405441
在心理健康教育领域,抑郁已成为影响个体心理健康的重要指标之一。抑郁在临床上的特征包括情绪低落、精力减退、愉快感缺失,甚至出现迟滞和思维困难等现象[1]。近年来,我国青少年及大学生群体抑郁人数逐渐增多,根据《中国国民心理健康发展报告 ( 2021—2022 ) 》显示,我国18~24岁群体抑郁的检出率达24.1%,显著高于其他年龄组[2]。元分析的研究表明,我国大学生群体抑郁的检出率在13.25%~79.90%之间,而普通人群的检出率为5%~6%[3],长期处于抑郁状态不仅会影响大学生正常的人际交往功能,而且影响正常的社会功能发展,甚至会导致其出现自杀意念,诱发自杀行为等[4-5]
目前,国内对大学生抑郁的研究主要集中于变量中心的视角,鲜有研究从个体中心的思路来探讨大学生抑郁的变化模式。潜在转变分析( LTA )是从个体中心角度分析个体心理阶段性变化的常用方法,其从概率的角度通过转变矩阵估计不同类别的群体在相异的时间点潜在状态的变化,是潜在类别分析或潜在剖面分析的纵向扩展[6-8]。国外研究表明,长住疗养院居民的抑郁可分为多重抑郁症状组、抑郁情绪组、耗竭组及轻微症状组,且抑郁情绪组和耗竭组转变为轻微症状组的概率分别是22%和17%,但多重抑郁症状组转变为轻微症状组的概率仅7%[9];儿童及青少年心理健康问题可划分为正常组、情绪问题组及共病组,且相对于正常组,情绪问题组及共病组更容易出现抑郁情绪[10]。国内的相关研究表明,成年人抑郁可分为高抑郁症状组、低抑郁症状组及过渡组,且高抑郁症状组向低抑郁症状组转变的概率为8.4%,而低抑郁症状组向高抑郁症状组转变概率为17.5%[11];农民工抑郁可分为低抑郁症状组、积极预期过渡组、消极预期过渡组及高抑郁症状组,且高抑郁症状组向低抑郁症状组转变的概率为9.2%,而低抑郁症状组向高抑郁症状组转变概率为2.8%[12]。由此可见,诸多研究探讨了不同群体抑郁的潜在转变模式,而目前国内对于大学生群体抑郁的纵向变化尚比较缺乏。因此,很有必要从个体为中心的视角来探讨我国大学生抑郁的转变模式。
此外,以往的横断研究表明,网络社会排斥是导致大学生抑郁的重要影响因素[13],童年期有留守经历大学生整体的心理健康水平要低于无留守经历大学生[14]。性别对于大学生抑郁的影响存在矛盾,一些研究认为大学生抑郁不存在性别差异[15],而一些研究则认为存在差异[16]。然而,鲜有研究探讨这些因素对大学生抑郁潜在转变过程中产生的影响。因此,本研究拟以大学生为被试,并采用个体中心的研究方法,主要考察大学生抑郁的潜在转变模式以及网络社会排斥、性别等因素对转变概率的影响。
样本估算:依据公式n=( tα/2 · s/δ )2,其中tα/2t分布中α=0.05的临界值,s为以往相关研究中的标准差,δ为容许误差。根据以往研究,s为0.51[17]δ为10%[18]tα/2为1.96,计算得n=100,故本研究至少需要100人。采用整群方便抽样法,从江苏省、河南省、福建省、甘肃省、辽宁省、黑龙江省及内蒙古自治区等省市的7所本科院校共抽取2 000名本科生进行为期4个月的纵向追踪。于2019年9月 (T1) 收回有效问卷1 734份,12月 (T2) 收回有效问卷1 476份,被试流失258人。其中,有留守经历大学生285人,无留守经历1 191人;男生793人,女生941人;被试年龄范围为16~24岁,平均年龄为(19.39±1.44)岁,年龄信息缺失23人。考虑到被试的流失,本研究继而对被试流失进行缺失机制的检验。将第2次的研究数据按照流失与非流失分为两组,对这两组与第1次测量中无缺失的数据(抑郁、网络社会排斥)进行独立样本t检验,结果表明,在抑郁得分上,两组数据无显著差异(t=0.92, P=0.36);在网络社会排斥得分上,两组数据也无显著差异(t=0.62, P=0.54 ),这说明本研究流失的数据属于完全随机缺失,可以进行进一步的潜在转变分析。研究对象的纳入标准:(1)能够理解问卷内容,自愿参与本研究;(2)在校本科生。排除标准:(1)不同意问卷调查;(2)问卷填写不足80%。本研究已获得内蒙古师范大学心理学院伦理审查委员会批准( 2024040201 ),研究对象均已签署知情同意书。
采用抑郁量表(depression scale, DS)[19]进行调查,共7个条目,采用1 (完全不符合) ~ 5 (完全符合) 点计分,单维结构,无反向计分条目。被试得分越高,说明抑郁程度越高。在本研究2次追踪过程中,问卷整体的Cronbach α系数分别为0.88、0.85。为使研究结果更为精确,本研究按照潜在类别分析的要求,将作答为“1 (完全不符合)”和“2 (不符合)”的转化为“0 (无抑郁) ”,将作答“3 (有时符合)、4 (符合) 、5 (完全符合) ”转化为“1 (有抑郁) ”[20]
采用网络社会排斥问卷(cyber ostracism questionnaire, COQ )[21] 进行调查,共14个条目,采用1 (从未) ~ 5 (总是) 点计分,无反向计分条目,分为网络群体聊天、网络个人空间及网络个体聊天三个维度。在本研究两次追踪过程中,问卷整体的Cronbach α系数分别为0.91、0.96。
采用 SPSS25.0、Mplus8.3软件进行数据处理。对被试流失的缺失值采用独立样本t检验,采用潜在类别分析与潜在转变分析方法探究大学生抑郁的潜在类别及不同类别间的转变概率,检验水准α=0.05。由于本研究要求被试报告的变量均属于负性量表,需要检验共同方法偏差问题。采用Harman单因素法,将所有量表题目进行未旋转探索性因素分析,结果发现,2次追踪的数据分别有3个和4个特征根大于1的公共因子被提出,且第1个公因子解释率分别为19.5%和21.6%,均小于40%的标准[22],说明本研究不存在严重的共同方法偏差问题。
本研究对2次大学生抑郁的发生率进行统计,见表1。发生率的计算为有抑郁人数/总人数×100%。结果发现,大学生整体抑郁的发生率具有下降趋势,但部分抑郁类型也呈现出弱升高趋势(W6)。
两个时间点大学生抑郁条目分别进行潜在类别分析,拟合信息包括AIC、BIC、aBIC、Entropy、LMR(P)及BLRT(P)。结果表明,AIC、BIC、aBIC均随着类别数目的增多而逐步减小,其中两个模型在第2个类别时减少的幅度最大,Entropy指标在第2个模型均最高,LMR(P)和BLRT(P)也在第2个模型时均显著优于其它模型,见表2。因此,选取2类别的潜在类别模型。
对2个时间点的潜在类别进一步分析,分别将7个条目中均选择有抑郁选项的概率命名,见图12。从图中可以看出,这7个选项被划分为2个类别,第1个类别的条件概率明显高于第2个类别,将第1个类别命名为“低抑郁症状组”,第2个类别命名为“高抑郁症状组”。从图1图2的变化过程可以看出,低抑郁症状组随着时间的变化,其整体概率略有提高,其在2次时间点上人数分别为1 392(80.3%)和1 240人(84.1% ),而高抑郁症状组的概率则略有降低,其在2次时间点上的人数分别为342(19.7%)和236人(15.9% )。
在2次潜类别分析的基础上,本研究继而分析在2次时间点上大学生抑郁的潜在转变过程。结果显示,低抑郁症状组保持原潜在状态的概率为47.3%,高抑郁症状组的概率为92.4%;低抑郁症状组向高抑郁症状组转变的概率为52.7%,高抑郁症状组向低抑郁症状组转变的概率为7.6%。
排斥、性别、年龄、年级、专业及留守经历对大学生抑郁潜在类别的影响,以低抑郁症状组为参照组,进行logistic回归分析,得出被试属于高抑郁症状组的概率与低抑郁症状组的概率在协变量影响下的优势比(OR)。结果发现,与女生相比,男生出现高抑郁症状的优势比比女生出现高抑郁症状的优势比降低了0.27倍;年龄每增加1岁,出现高抑郁症状的优势比降低0.12倍;网络社会排斥水平每增加1分,出现高抑郁症状的优势比增加0.94倍。留守经历、年级与专业对大学生抑郁潜在类别的影响均不显著(P>0.05)。见表3
此外,本研究进一步探讨各因素对大学生抑郁类型转变的影响。将保持原潜在状态的大学生抑郁作为参照组,优势比指被试转变到其它组的概率与保持原组概率的变化之比。优势比大于1表示在协变量影响下,被试发生该转变的概率增加,反之则减小。从表4可以看出,男生由高抑郁症状组转变为低抑郁症状组的概率减小(OR=0.35);年龄每增加1岁,低抑郁症状组转变为高抑郁症状组的概率减小(OR= 0.88);网络社会排斥水平每增加1分,低抑郁症状组转变为高抑郁症状组的概率增加(OR=1.74)。
本研究发现,在4个月的追踪过程中,大学生抑郁的发生率还是相对较高,比如在T2时间点,依然高出《中国国民心理健康发展报告(2021—2022)》中18~24岁群体9.5个百分点[2],说明我国大学生的心理健康问题面临着严峻的挑战,未来对于大学生群体抑郁的防治和干预工作需要进一步强化。当然,危中有机的是,大学生抑郁得分呈逐渐下降趋势,这与以往的研究相对一致[23],同时也说明这些高校的心理健康工作是有成效的。此外,本研究潜在转变分析发现,大学生抑郁可以分为低抑郁症状组和高抑郁症状组2个潜在类别,高抑郁症状组保持原潜在状态的概率达92.4%,这说明高抑郁症状组的大学生抑郁状态已经比较持久了,这类大学生群体需要高校和家庭建立联动跟踪机制,需要及时地运用心理辅导技术进行适时干预,确保将危机事件发生的概率降到最低。并且,针对这类群体应以感恩、生命意义感等主题作为落脚点[24],制定干预策略,为未来潜在问题性个体提供可参考的案例与理论。同时,本研究发现,低抑郁症状组转变到高抑郁症状组的概率为52.7%,说明低抑郁症状组中有部分大学生具有潜在的抑郁发生的风险,对于这部分大学生而言,不仅要提升自身心理韧性水平,而且要多参加活动锻炼,进而提升应对风险能力,降低抑郁水平。
本研究发现,与女大学生相比,男大学生更不容易出现抑郁状况,且男生由高抑郁症状组转变为低抑郁症状组的概率减小,女性心思细腻,容易敏感,对一件事情容易反复思考,进而影响情绪状态,也就容易引发抑郁。同时,本研究发现,随着年龄的升高,大学生抑郁出现高抑郁症状的概率和优势比均降低,而随着网络社会排斥水平的升高,出现高抑郁症状的优势比以及由低抑郁症状组转变为高抑郁症状组的概率均升高,这提示我们年龄可能是诱发大学生出现抑郁的抑制因素,而网络社会排斥是促进因素,未来对于大学生抑郁群体可以考虑通过降低网络社会排斥水平进行干预治疗。
本研究存在两方面的不足。一方面,限于研究者的精力,本研究追踪的时间较短,未来可以考虑更长时间的追踪,从而获得更为精准的结果;另一方面,本研究没有充分考虑到与大学生抑郁相关的前因和后效,未来的研究可以进一步探讨。
  • 国家社会科学基金一般项目(23BSH142)
  • 内蒙古自然科学基金面上项目(2022MS03004)
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2024年第51卷第23期
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doi: 10.20043/j.cnki.MPM.202405441
  • 接收时间:2024-05-24
  • 首发时间:2026-03-18
  • 出版时间:2024-12-10
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  • 收稿日期:2024-05-24
基金
国家社会科学基金一般项目(23BSH142)
内蒙古自然科学基金面上项目(2022MS03004)
作者信息
    1.内蒙古师范大学心理学院,内蒙古 呼和浩特 010022
    2.内蒙古高校人文社科重点研究基地心理健康教育研究与服务基地,内蒙古 呼和浩特 010022

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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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