Article(id=1320435226623459414, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1320435149154669015, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202508044, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755273600000, receivedDateStr=2025-08-16, revisedDate=1762531200000, revisedDateStr=2025-11-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1792745890527, onlineDateStr=2026-10-23, pubDate=1769702400000, pubDateStr=2026-01-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792745890527, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792745890527, creator=13701087609, updateTime=1792745890527, updator=13701087609, issue=Issue{id=1320435149154669015, tenantId=1146029695717560320, journalId=1319373162502258839, year='2026', volume='48', issue='2', pageStart='107', pageEnd='250', issueExtLink='null', onlineDate='null', pubDate='1769702400000', pubDateStr='2026-01-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792745872057, creator='13701087609', updateTime=1792749692151, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320451171886682715, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1320435149154669015, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320451171886682716, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1320435149154669015, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=242, endPage=250, ext={EN=ArticleExt(id=1320435226900283480, articleId=1320435226623459414, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=An analysis of factors influencing the development of depressive symptoms among postgraduate students: The roles of adaptation challenges, employment stress, and romantic troubles, columnId=1320435226828980311, journalTitle=Journal of Army Medical University, columnName=Medical Psychology, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the transition patterns of latent classes of depressive symptoms among postgraduate students and their influencing factors.

Methods

A prospective cohort study was conducted. Two waves of survey were carried out on 5 577 postgraduate students from an university in Hubei Province and another one in Anhui Province in October 2021 (T1) and October 2022 (T2). Self-Rating Depression Scale and College Students Mental Health Screening Scale were applied respectively to measure the severities of depressive symptoms and developmental distress. The optimal classification model was determined through the fitting indicators of the latent class model, mainly including indicators such as Entropy value, Lo-Mendell-Rubin (LMR), and Bootstrapped Likelihood Ratio Test (BLRT). Latent transition analysis was used to obtain the transition probabilities of latent class changes at different time points, and multivariate logistic regression analysis was employed to identify the factors influencing the latent class transition of depressive symptoms in the postgraduate students.

Results

① The depressive symptoms could be classified into 3 categories among the postgraduate students (T1: Entropy=0.85, LMRP<0.05, BLRTP<0.001; T2: Entropy=0.88, LMRP<0.001, BLRT P<0.001), that is, a non-depressive symptom group (66.0% vs 64.3%), a mild depressive symptom group (25.6% vs 26.9%), and a moderate depressive symptom group (8.4% vs 8.8%). ② The probabilities of each latent class remaining in the original group at time point T2 were 84.1%, 56.9%, and 26.6%, respectively. The probability of transition from moderate to mild depressive symptom group was 41.3%, and the probability from mild to non-depressive symptom group was 29.3%.③ Adaptation challenges(OR=1.08, 95%CI: 1.02 to 1.16, P<0.05) and employment stress (OR=1.05, 95CI: 1.01 to 1.10, P<0.05) were promoting factors for the transition from non-depressive to mild depressive symptom group. Romantic troubles (OR=0.86, 95%CI: 0.76 to 0.99, P<0.05) was an obstructive factor for the transition from moderate to non-depressive symptom group.

Conclusion

There are 3 latent categories of depressive symptoms among postgraduate students. an obstructive factor for the transition from moderate to non-depressive symptom group.Adaptation challenges, employment stress and romantic troubles are important factors for the development of depressive symptoms.

, authors=Gui LUO1, 2, Bin GAO3, Jingjing LIANG2, 4, authorsList=Gui LUO, Bin GAO, Jingjing LIANG, authorCompany=null, correspAuthors=Bin GAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2026 Journal of Army Medical University, copyrightOwner=Editorial Office of Journal of Army Medical University, 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=1320435228968075356, articleId=1320435226623459414, tenantId=1146029695717560320, journalId=1319373162502258839, language=CN, title=适应困难、就业压力与恋爱困扰:研究生抑郁症状发展的影响因素分析, columnId=1320435226988363865, journalTitle=陆军军医大学学报, columnName=论著·医学心理研究, runingTitle=null, highlight=null, articleAbstract=
目的

探究研究生抑郁症状潜在类别的转变规律及其影响因素。

方法

采用前瞻性队列研究,于2021年10月(T1)和2022年10月(T2)对湖北省、安徽省两所高校的5 577名研究生进行2次调查。采用抑郁自评量表测量抑郁情绪水平,并使用大学生心理健康筛查量表测量发展性困扰。通过潜在类别模型的拟合指标确定最优分类模型,主要包括Entropy值、Lo-Mendell-Rubin(LMR)、Bootstrapped似然比检验(BLRT)等指标。通过潜在转变分析得出不同时间点潜在类别变化的转变概率,采用多项Logistic回归分析确定影响研究生抑郁症状潜在类别转变的因素。

结果

①研究生抑郁症状可分为3个类别(T1:Entropy=0.85,LMRP<0.05,BLRT P<0.001;T2:Entropy=0.88,LMR P<0.001,BLRT P<0.001),分别为无抑郁症状组(66.0% vs 64.3%)、轻度抑郁症状组(25.6% vs 26.9%)和中度抑郁症状组(8.4% vs 8.8%)。②各潜在类别在T2时间点保持在原组的概率依次为84.1%、56.9%和26.6%,中度抑郁症状组向轻度抑郁症状组转变的概率是41.3%,轻度抑郁症状组向无抑郁症状组转变的概率是29.3%。③适应困难(OR=1.08,95%CI:1.02~1.16,P<0.05)和就业压力(OR=1.05,95%CI:1.01~1.10,P<0.05)是无抑郁症状组转变为轻度抑郁症状组的促进因素,恋爱困扰(OR=0.86,95%CI:0.76~0.99,P<0.05)是中度抑郁症状组转变为无抑郁症状组的阻碍因素。

结论

研究生抑郁症状存在3个类别,适应困难、就业压力和恋爱困扰是影响抑郁症状发展的重要因素。

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高 斌,E-mail:
GAO Bin,E-mail:
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T1: The year of 2021; T2: The year of 2022

, figureFileSmall=DrHKygXyPSYOwa/lBg9jbQ==, figureFileBig=V9o2+qDfbanpgLm2ix3/Zw==, tableContent=null), ArticleFig(id=1320435231375605892, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=EN, label=Table 1, caption=

Demographic data of the participants (n=5 577)

, figureFileSmall=null, figureFileBig=null, tableContent=
VariablenProportion(%)
Gender
Male3 77867.74
Female1 79932.26
Grade
First-year postgraduate student2 91652.29
Second-year postgraduate student2 66147.71
Ethnic group
Han ethnicity5 48098.26
Ethnic minorities971.74
Place of origin
Town2 50744.95
Rural areas3 06254.91
Missing value80.14
Whether the child is an only child
Only child2 39443.93
Not an only child3 17556.93
Missing value80.14
), ArticleFig(id=1320435231434326149, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=CN, label=表1, caption=

研究对象的人口学信息 (n=5 577)

, figureFileSmall=null, figureFileBig=null, tableContent=
VariablenProportion(%)
Gender
Male3 77867.74
Female1 79932.26
Grade
First-year postgraduate student2 91652.29
Second-year postgraduate student2 66147.71
Ethnic group
Han ethnicity5 48098.26
Ethnic minorities971.74
Place of origin
Town2 50744.95
Rural areas3 06254.91
Missing value80.14
Whether the child is an only child
Only child2 39443.93
Not an only child3 17556.93
Missing value80.14
), ArticleFig(id=1320435231493046406, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=EN, label=Table 2, caption=

Fitting indicators of the latent class model of depressive symptoms in postgraduate students

, figureFileSmall=null, figureFileBig=null, tableContent=
TimeModelNpLLAICBICaBICEntropyLMR(P)BLRT(P)Class probability(%)
T11-Class8-46 995.1294 006.2494 059.2594 033.83————
2-Class13-44 155.5188 337.0288 423.1688 381.850.84<0.001<0.00172.5/27.5
3-Class18-42 895.1785 826.3385 945.6085 888.410.850.03<0.00166.0/25.6/8.4
4-Class23-42 025.3984 096.7884 249.1984 176.100.89<0.001<0.00164.9/1.2/8.3/25.6
T21-Class8-47 295.8594 607.7194 660.7294 635.30————
2-Class13-43 895.3287 816.6487 902.7887 861.470.85<0.001<0.00168.8/31.2
3-Class18-42 498.5085 033.0185 152.2885 095.090.88<0.001<0.00164.3/26.9/8.8
4-Class23-41 679.1183 404.2283 556.6383 483.540.920.13<0.00160.6/2.8/16.2/20.4
), ArticleFig(id=1320435233304985736, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=CN, label=表2, caption=

研究生抑郁症状潜在类别模型的拟合指标

, figureFileSmall=null, figureFileBig=null, tableContent=
TimeModelNpLLAICBICaBICEntropyLMR(P)BLRT(P)Class probability(%)
T11-Class8-46 995.1294 006.2494 059.2594 033.83————
2-Class13-44 155.5188 337.0288 423.1688 381.850.84<0.001<0.00172.5/27.5
3-Class18-42 895.1785 826.3385 945.6085 888.410.850.03<0.00166.0/25.6/8.4
4-Class23-42 025.3984 096.7884 249.1984 176.100.89<0.001<0.00164.9/1.2/8.3/25.6
T21-Class8-47 295.8594 607.7194 660.7294 635.30————
2-Class13-43 895.3287 816.6487 902.7887 861.470.85<0.001<0.00168.8/31.2
3-Class18-42 498.5085 033.0185 152.2885 095.090.88<0.001<0.00164.3/26.9/8.8
4-Class23-41 679.1183 404.2283 556.6383 483.540.920.13<0.00160.6/2.8/16.2/20.4
), ArticleFig(id=1320435233401454729, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=EN, label=Table 3, caption=

Characteristics of latent class of depressive symptoms among postgraduate students in each dimension of the SDS scale (x¯±s)

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TimeLatent classAffective symptomsSomatic symptomsPsychomotor symptomsPsychological symptomsDepression severity index
T1Class 12.16±0.4212.25±2.123.23±0.9413.12±2.860.38
Class 22.35±0.4816.21±2.254.89±0.8320.46±3.590.55
Class 34.61±0.9717.68±3.795.24±1.2520.93±4.160.61
T2Class 12.13±0.3611.83±2.153.07±0.8912.62±2.870.37
Class 22.34±0.4716.01±2.364.83±0.8020.56±3.760.55
Class 34.52±0.9217.73±3.485.12±1.1520.33±3.950.60
), ArticleFig(id=1320435233493729418, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=CN, label=表3, caption=

研究生抑郁症状潜在类别在SDS量表各维度上的特征(x¯±s)

, figureFileSmall=null, figureFileBig=null, tableContent=
TimeLatent classAffective symptomsSomatic symptomsPsychomotor symptomsPsychological symptomsDepression severity index
T1Class 12.16±0.4212.25±2.123.23±0.9413.12±2.860.38
Class 22.35±0.4816.21±2.254.89±0.8320.46±3.590.55
Class 34.61±0.9717.68±3.795.24±1.2520.93±4.160.61
T2Class 12.13±0.3611.83±2.153.07±0.8912.62±2.870.37
Class 22.34±0.4716.01±2.364.83±0.8020.56±3.760.55
Class 34.52±0.9217.73±3.485.12±1.1520.33±3.950.60
), ArticleFig(id=1320435233716027531, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=EN, label=Table 4, caption=

Probability of transition between latent class of depressive symptoms among postgraduate students (%)

, figureFileSmall=null, figureFileBig=null, tableContent=
T1T2
Non-depressive symptom groupMild depressive symptom groupModerate depressive symptom group
Non-depressive symptom group84.112.23.7
Mild depressive symptom group29.356.913.8
Moderate depressive symptom group32.141.326.6
), ArticleFig(id=1320435233808302220, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=CN, label=表4, caption=

研究生抑郁症状潜在类别之间的转变概率 (%)

, figureFileSmall=null, figureFileBig=null, tableContent=
T1T2
Non-depressive symptom groupMild depressive symptom groupModerate depressive symptom group
Non-depressive symptom group84.112.23.7
Mild depressive symptom group29.356.913.8
Moderate depressive symptom group32.141.326.6
), ArticleFig(id=1320435234135457933, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=EN, label=Table 5, caption=

Multivariate logistic regression analysis of transition of depressive symptom subtypes in postgraduate students by predictor variables

, figureFileSmall=null, figureFileBig=null, tableContent=
Predictor variableBSEWaldOR95%CI
Adaptation challenges
T1 Non-DSG→T2 Mild DSG0.080.036.36a1.08(1.02~1.16)
T1 Non-DSG→T2 Moderate DSG0.040.060.591.04(0.94~1.17)
T1 Mild DSG→T2 Non-DSG-0.110.048.13b0.89(0.83~0.97)
T1 Mild DSG→T2 Moderate DSG-0.030.060.390.97(0.87~1.08)
T1 Moderate DSG→T2 Non-DSG-0.140.082.630.87(0.74~1.03)
T1 Moderate DSG→T2 Mild DSG0.060.080.671.07(0.92~1.24)
Interpersonal distress
T1 Non-DSG→T2 Mild DSG-0.010.040.011.00(0.93~1.07)
T1 Non-DSG→T2 Moderate DSG0.010.060.041.01(0.89~1.15)
T1 Mild DSG→T2 Non-DSG-0.010.050.070.99(0.90~1.08)
T1 Mild DSG→T2 Moderate DSG0.030.060.161.03(0.91~1.16)
T1 Moderate DSG→T2 Non-DSG-0.050.090.250.95(0.79~1.15)
T1 Moderate DSG→T2 Mild DSG-0.080.090.850.92(0.78~1.10)
Academic pressure
T1 Non-DSG→T2 Mild DSG-0.010.030.290.99(0.94~1.04)
T1 Non-DSG→T2 Moderate DSG0.010.050.021.00(0.92~1.10)
T1 Mild DSG→T2 Non-DSG-0.020.030.380.98(0.92~1.05)
T1 Mild DSG→T2 Moderate DSG0.020.050.191.02(0.93~1.13)
T1 Moderate DSG→T2 Non-DSG-0.070.070.940.93(0.81~1.07)
T1 Moderate DSG→T2 Mild DSG0.040.070.251.04(0.90~1.19)
Employment stress
T1 Non-DSG→T2 Mild DSG0.050.025.09a1.05(1.01~1.10)
T1 Non-DSG→T2 Moderate DSG-0.020.040.170.98(0.91~1.07)
T1 Mild DSG→T2 Non-DSG0.010.030.211.02(0.95~1.07)
T1 Mild DSG→T2 Moderate DSG0.010.050.031.00(0.92~1.10)
T1 Moderate DSG→T2 Non-DSG0.010.070.041.01(0.89~1.15)
T1 Moderate DSG→T2 Mild DSG0.010.060.011.00(0.89~1.13)
Romantic troubles
T1 Non-DSG→T2 Mild DSG0.020.030.391.02(0.96~1.08)
T1 Non-DSG→T2 Moderate DSG0.040.050.831.05(0.95~1.15)
T1 Mild DSG→T2 Non-DSG0.020.030.261.02(0.95~1.09)
T1 Mild DSG→T2 Moderate DSG-0.040.050.750.96(0.87~1.06)
T1 Moderate DSG→T2 Non-DSG-0.150.074.72a0.86(0.76~0.99)
T1 Moderate DSG→T2 Mild DSG-0.120.073.340.89(0.78~1.01)
), ArticleFig(id=1320435234273869966, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1320435226623459414, language=CN, label=表5, caption=

预测变量对研究生抑郁症状亚型转变的多项Logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
Predictor variableBSEWaldOR95%CI
Adaptation challenges
T1 Non-DSG→T2 Mild DSG0.080.036.36a1.08(1.02~1.16)
T1 Non-DSG→T2 Moderate DSG0.040.060.591.04(0.94~1.17)
T1 Mild DSG→T2 Non-DSG-0.110.048.13b0.89(0.83~0.97)
T1 Mild DSG→T2 Moderate DSG-0.030.060.390.97(0.87~1.08)
T1 Moderate DSG→T2 Non-DSG-0.140.082.630.87(0.74~1.03)
T1 Moderate DSG→T2 Mild DSG0.060.080.671.07(0.92~1.24)
Interpersonal distress
T1 Non-DSG→T2 Mild DSG-0.010.040.011.00(0.93~1.07)
T1 Non-DSG→T2 Moderate DSG0.010.060.041.01(0.89~1.15)
T1 Mild DSG→T2 Non-DSG-0.010.050.070.99(0.90~1.08)
T1 Mild DSG→T2 Moderate DSG0.030.060.161.03(0.91~1.16)
T1 Moderate DSG→T2 Non-DSG-0.050.090.250.95(0.79~1.15)
T1 Moderate DSG→T2 Mild DSG-0.080.090.850.92(0.78~1.10)
Academic pressure
T1 Non-DSG→T2 Mild DSG-0.010.030.290.99(0.94~1.04)
T1 Non-DSG→T2 Moderate DSG0.010.050.021.00(0.92~1.10)
T1 Mild DSG→T2 Non-DSG-0.020.030.380.98(0.92~1.05)
T1 Mild DSG→T2 Moderate DSG0.020.050.191.02(0.93~1.13)
T1 Moderate DSG→T2 Non-DSG-0.070.070.940.93(0.81~1.07)
T1 Moderate DSG→T2 Mild DSG0.040.070.251.04(0.90~1.19)
Employment stress
T1 Non-DSG→T2 Mild DSG0.050.025.09a1.05(1.01~1.10)
T1 Non-DSG→T2 Moderate DSG-0.020.040.170.98(0.91~1.07)
T1 Mild DSG→T2 Non-DSG0.010.030.211.02(0.95~1.07)
T1 Mild DSG→T2 Moderate DSG0.010.050.031.00(0.92~1.10)
T1 Moderate DSG→T2 Non-DSG0.010.070.041.01(0.89~1.15)
T1 Moderate DSG→T2 Mild DSG0.010.060.011.00(0.89~1.13)
Romantic troubles
T1 Non-DSG→T2 Mild DSG0.020.030.391.02(0.96~1.08)
T1 Non-DSG→T2 Moderate DSG0.040.050.831.05(0.95~1.15)
T1 Mild DSG→T2 Non-DSG0.020.030.261.02(0.95~1.09)
T1 Mild DSG→T2 Moderate DSG-0.040.050.750.96(0.87~1.06)
T1 Moderate DSG→T2 Non-DSG-0.150.074.72a0.86(0.76~0.99)
T1 Moderate DSG→T2 Mild DSG-0.120.073.340.89(0.78~1.01)
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适应困难、就业压力与恋爱困扰:研究生抑郁症状发展的影响因素分析
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骆贵 1, 2 , 高斌 3 , 梁晶晶 2, 4
陆军军医大学学报 | 论著·医学心理研究 2026,48(2): 242-250
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陆军军医大学学报 |论著·医学心理研究 2026 , 48 (2) : 242 -250
适应困难、就业压力与恋爱困扰:研究生抑郁症状发展的影响因素分析
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骆贵1, 2, 高斌3 , 梁晶晶2, 4
作者信息
  • 1华中师范大学:大学生心理健康教育中心,湖北 武汉
  • 2华中师范大学:心理学院,湖北 武汉
  • 3湖北民族大学教师教育学院,湖北 恩施
  • 4云南农业大学心理健康教育与咨询中心,云南 昆明
通讯作者:
高 斌,E-mail:
GAO Bin,E-mail:
An analysis of factors influencing the development of depressive symptoms among postgraduate students: The roles of adaptation challenges, employment stress, and romantic troubles
Gui LUO1, 2, Bin GAO3 , Jingjing LIANG2, 4
Affiliations
  • 1Mental Health Center,
  • 2School of Psychology, Central China Normal University, Wuhan, Hubei
  • 3School of Teacher Education, Hubei Minzu University, Enshi, Hubei
  • 4Mental Health Education and Conseling Center, Yunnan Agricultural University, Kunming, Yunnan, China
出版时间: 2026-01-30 doi: 10.16016/j.2097-0927.202508044
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目的

探究研究生抑郁症状潜在类别的转变规律及其影响因素。

方法

采用前瞻性队列研究,于2021年10月(T1)和2022年10月(T2)对湖北省、安徽省两所高校的5 577名研究生进行2次调查。采用抑郁自评量表测量抑郁情绪水平,并使用大学生心理健康筛查量表测量发展性困扰。通过潜在类别模型的拟合指标确定最优分类模型,主要包括Entropy值、Lo-Mendell-Rubin(LMR)、Bootstrapped似然比检验(BLRT)等指标。通过潜在转变分析得出不同时间点潜在类别变化的转变概率,采用多项Logistic回归分析确定影响研究生抑郁症状潜在类别转变的因素。

结果

①研究生抑郁症状可分为3个类别(T1:Entropy=0.85,LMRP<0.05,BLRT P<0.001;T2:Entropy=0.88,LMR P<0.001,BLRT P<0.001),分别为无抑郁症状组(66.0% vs 64.3%)、轻度抑郁症状组(25.6% vs 26.9%)和中度抑郁症状组(8.4% vs 8.8%)。②各潜在类别在T2时间点保持在原组的概率依次为84.1%、56.9%和26.6%,中度抑郁症状组向轻度抑郁症状组转变的概率是41.3%,轻度抑郁症状组向无抑郁症状组转变的概率是29.3%。③适应困难(OR=1.08,95%CI:1.02~1.16,P<0.05)和就业压力(OR=1.05,95%CI:1.01~1.10,P<0.05)是无抑郁症状组转变为轻度抑郁症状组的促进因素,恋爱困扰(OR=0.86,95%CI:0.76~0.99,P<0.05)是中度抑郁症状组转变为无抑郁症状组的阻碍因素。

结论

研究生抑郁症状存在3个类别,适应困难、就业压力和恋爱困扰是影响抑郁症状发展的重要因素。

研究生  /  抑郁症状  /  潜在类别分析  /  潜在转变分析
Objective

To explore the transition patterns of latent classes of depressive symptoms among postgraduate students and their influencing factors.

Methods

A prospective cohort study was conducted. Two waves of survey were carried out on 5 577 postgraduate students from an university in Hubei Province and another one in Anhui Province in October 2021 (T1) and October 2022 (T2). Self-Rating Depression Scale and College Students Mental Health Screening Scale were applied respectively to measure the severities of depressive symptoms and developmental distress. The optimal classification model was determined through the fitting indicators of the latent class model, mainly including indicators such as Entropy value, Lo-Mendell-Rubin (LMR), and Bootstrapped Likelihood Ratio Test (BLRT). Latent transition analysis was used to obtain the transition probabilities of latent class changes at different time points, and multivariate logistic regression analysis was employed to identify the factors influencing the latent class transition of depressive symptoms in the postgraduate students.

Results

① The depressive symptoms could be classified into 3 categories among the postgraduate students (T1: Entropy=0.85, LMRP<0.05, BLRTP<0.001; T2: Entropy=0.88, LMRP<0.001, BLRT P<0.001), that is, a non-depressive symptom group (66.0% vs 64.3%), a mild depressive symptom group (25.6% vs 26.9%), and a moderate depressive symptom group (8.4% vs 8.8%). ② The probabilities of each latent class remaining in the original group at time point T2 were 84.1%, 56.9%, and 26.6%, respectively. The probability of transition from moderate to mild depressive symptom group was 41.3%, and the probability from mild to non-depressive symptom group was 29.3%.③ Adaptation challenges(OR=1.08, 95%CI: 1.02 to 1.16, P<0.05) and employment stress (OR=1.05, 95CI: 1.01 to 1.10, P<0.05) were promoting factors for the transition from non-depressive to mild depressive symptom group. Romantic troubles (OR=0.86, 95%CI: 0.76 to 0.99, P<0.05) was an obstructive factor for the transition from moderate to non-depressive symptom group.

Conclusion

There are 3 latent categories of depressive symptoms among postgraduate students. an obstructive factor for the transition from moderate to non-depressive symptom group.Adaptation challenges, employment stress and romantic troubles are important factors for the development of depressive symptoms.

postgraduate students  /  depressive symptoms  /  latent class analysis  /  latent transition analysis
骆贵, 高斌, 梁晶晶. 适应困难、就业压力与恋爱困扰:研究生抑郁症状发展的影响因素分析. 陆军军医大学学报, 2026 , 48 (2) : 242 -250 . DOI: 10.16016/j.2097-0927.202508044
Gui LUO, Bin GAO, Jingjing LIANG. An analysis of factors influencing the development of depressive symptoms among postgraduate students: The roles of adaptation challenges, employment stress, and romantic troubles[J]. Journal of Army Medical University, 2026 , 48 (2) : 242 -250 . DOI: 10.16016/j.2097-0927.202508044
近年来,随着研究生教育规模的持续扩大和培养质量的逐步提升,研究生群体面临着显著的心理健康风险[1]。抑郁症状是指一组症状综合征,核心特征包括持久的心境低落、兴趣减退及快感丧失等,并伴有不同程度的认知和行为改变[2]。国外一项元分析结果显示,在过去40年中,研究生抑郁症状的患病率高达34%[3]。国内元分析研究发现,研究生抑郁症状的检出率为21%[4]。抑郁症状不仅损害个体的身心健康、学业成绩和人际交往,还与自杀风险密切相关[5-7]。若抑郁症状长期持续且未得到有效干预,极易从亚临床状态发展为临床抑郁症,最终可能诱发严重的心理危机,从而威胁生命安全。因此,深入探究研究生群体的抑郁问题,并精准识别其风险因素,对于维护研究生心理健康以及提升高层次人才培养质量具有重要的现实意义。
目前,学术界对研究生抑郁症状的研究多采用以变量为中心的视角,较少从以个体为中心的视角探讨其发展规律[8-10]。近年来,以潜在转变分析(latent transition analysis,LTA)为代表的个体中心方法在心理学领域逐渐受到关注并得到广泛应用研究。潜在转变分析是潜在类别分析在纵向数据上的拓展,它能够识别多个时间点上个体反应模式所归属的潜在类别,并进一步分析不同潜在类别随时间变化的转变趋势[11]。研究表明,中学生、大学生以及成年早期人群抑郁症状均存在群体异质性,并呈现出多样化的发展模式[12-14]。然而,针对研究生群体抑郁发展的相关研究证据仍显不足。因此,采用以个体为中心的方法对研究生抑郁症状进行深入探讨显得尤为必要。
根据已有研究,研究生抑郁症状受多种因素的影响[15-16]。其中,发展性困扰已被识别为研究生群体抑郁症状的风险因素。此类困扰贯穿于研究生求学生涯的各个核心环节,具体表现为学业压力、职业发展压力、经济压力、恋爱压力以及导学关系冲突等[17-19]。目前关于发展性困扰与抑郁症状之间关联的实证证据大多基于横断面研究,鲜有从队列研究范式探讨发展性困扰对研究生抑郁症状发展的影响。
因此,本研究基于以个体为中心的分析视角,通过为期2年的队列调查,旨在探究研究生抑郁症状的潜在类别、发展模式及影响因素,以期为研究生抑郁症状的预防和干预提供理论支持。
本研究采用前瞻性队列设计。于2021年10月(T1)从湖北省、安徽省两所高校招募了5 637名研究生建立基线队列。随后对该队列进行为期1年的随访,并于2022年10月(T2)完成第2次数据收集。T1与T2分别回收有效问卷5 637份与5 577份。将2次调查均参与且问卷有效的个体纳入分析,最终有效被试为5 577人,随访率为98.94%。本研究已获华中师范大学生命科学伦理审查委员会批准(CCNU-IRB-202108008b)。
抑郁自评量表(Self-Rating Depression Scale,SDS)由ZUNG等[20]编制,共20个条目,主要用于评估调查对象的抑郁症状。量表包括4个维度,分别为精神病性-情感症状、躯体性障碍、精神运动性障碍和抑郁的心理障碍。量表按照李克特4级评分方法,从“偶尔”到“持续”分别赋值为1~4分。通过计算总分除以80得到抑郁严重程度指数,该指数越高,表明个体的抑郁症状越严重。具体划分标准如下:指数<0.5代表无抑郁,0.5~0.6代表轻度抑郁,>0.6~0.7代表中度抑郁,>0.7则代表重度抑郁[21]。本研究选取T1和T2时间点的抑郁症状进行调查,在2次测评中Cronbach’s α系数分别为0.86和0.88。
大学生心理健康筛查量表(College Students Mental Health Screening Scale,CSMHSS)由方晓义等[22]编制,共96个条目,主要用于评估调查对象的心理健康状况。量表共分为3个层级的心理筛查:一级筛查聚焦于严重心理危机,二级筛查聚焦于一般心理问题,三级筛查则聚焦于发展性困扰。其中,发展性困扰包括适应困难、人际困扰、学业压力、就业压力和恋爱困扰5个维度,每个维度4个题目,共计20个条目。适应困难包含“不喜欢现在的学校”和“不适应大学的生活方式”等条目,人际困扰包含“感觉不受同学欢迎”和“和班里同学相处不愉快”等条目,学业压力包含“感觉学习有困难”和“担心考试”等条目,就业压力包含“担心找不到好工作”和“对自己的求职能力没有信心”等条目,恋爱困扰包含“谈恋爱对学习、生活造成负面影响”和“感觉为恋爱所累”等条目。量表题项从“一点也不像我”到“非常像我”按照李克特4级评分方法,分别赋值为1~4分。各维度题目累加计分,分数越高,代表发展性困扰越严重。该量表已经成为我国高校学生心理健康测评的主流工具,并且对研究生群体具有良好的适用性[23]。本研究在T1时间点对调查对象进行心理健康筛查,选取发展性困扰作为抑郁症状类别转变的预测变量。在本次测评中,该量表的Cronbach’s α系数为0.91。
采用Harman单因素检验法对测评问卷的全部条目进行分析。结果显示,在T1和T2时间点特值>1的因子数均有3个,最大公因子解释的总方差分别为28.56%和31.53%,均低于40%的临界值,因此本研究数据不存在明显的共同方法偏差。
本研究运用SPSS 22.0和Mplus7.0软件进行数据分析。定性资料采用频数(n)和百分比(%)表示,符合正态分布的计量资料使用x¯±s表示。首先,采用潜在类别分析模型探究研究生抑郁症状的亚组。模型拟合指标主要包括赤池信息准则(Akaike information criterion,AIC)、贝叶斯信息准则(Bayesian information criterion,BIC)、调整后贝叶斯信息准则(adjusted Bayesian information criterion,aBIC)、Entropy(>0.8分类精准度高)、Lo-Mendell-Rubin(LMR)与Bootstrapped似然比检验(Bootstrapped Likelihood Ratio Test,BLRT)的显著性。其次,运用潜在转变分析探究研究生抑郁症状亚组的转变概率。最后,以发展性困扰作为预测变量,将研究生抑郁症潜在类别的转变作为结果变量,纳入多项Logistic回归分析模型,探讨影响研究生抑郁症状潜在类别转变的因素,P<0.05为差异具有统计学意义。
采用一般人口学调查表收集被试信息(频数和百分比)。本研究共纳入5 577名被试,年龄为(23.33±1.49)岁。男生3 778人,女生1 799人。研一2 916人,研二2 661人。汉族5 480人,少数民族97人。城镇2 507人,农村3 062人,8人未报告。独生子女2 394人,非独生子女3 175人,8人未报告。见表1。
按照抑郁自评量表的标准,评估研究生抑郁症状的严重程度。结果显示,在T1时间点,研究生抑郁总体发生率为28.6%,其中轻度抑郁占17.5%,中度抑郁占9.7%,重度抑郁占1.4%。在T2时间点,研究生抑郁总体发生率为28.3%,其中轻度抑郁、中度抑郁和重度抑郁的比例分别为15.8%、11.5%和1.0%。提示研究生抑郁症状总体发生率在间隔1年后有所下降。
采用潜在类别分析,确定研究生抑郁症状的最优分类模型。结果显示,随分类数的增加,AIC、BIC、aBIC值均下降。在T2时间点,取4类别模型时LMR未达到显著水平,表明4类别模型的拟合指标不理想。将研究生抑郁症状划分为3个潜在类别时,T1与T2时间点LMR和BLRT数值均为显著水平(P<0.05),表明3类别模型显著优于2类别模型。同时Entropy值超过0.8,说明分类精准度较高。提示3类别模型为研究生抑郁症状的最优拟合模型。见表2。
采用SDS量表,计算抑郁症状潜在类别组的特征以及抑郁严重指数。根据3个潜在类别在SDS各维度上的平均得分,采用程度命名法,分别命名为无抑郁症状组、轻度抑郁症状组和中度抑郁症状组。类别1在抑郁症状的4个维度上平均得分处于最低水平(x¯:2.16~13.12),T1和T2时间点的抑郁严重程度指数分别为0.38和0.37,均低于0.5,表明该组研究生的抑郁症状最轻。结合得分特征,将其命名为“无抑郁症状组”。该组研究生人数在2个时间点上最多,占总体的比例分别为66.0%和64.3%。类别2在抑郁症状的4个维度上平均得分处于中间水平(x¯:2.35~20.46),T1和T2时间点的抑郁严重程度指数为0.55,均>0.5,结合得分特征,将其命名为“轻度抑郁症状组”。该组研究生人数介于类别1与类别3之间,人数占总体的比例随时间推移呈升高趋势,分别为25.6%和26.9%。类别3在抑郁症状的4个维度上平均得分处于最高水平(x¯:4.61~20.93),T1和T2时间点的抑郁严重程度指数分别为0.61和0.60,均高于0.5,表明类别3的抑郁程度超过轻度抑郁水平。结合得分特征,将其命名为“中度抑郁症状组”。该组研究生人数最少,人数占总体的比例随时间推移呈升高趋势,分别为8.4%和8.8%。提示研究生抑郁症状潜在类别组存在异质性差异。见图1、表3。
采用潜在转变分析,得出研究生抑郁症状潜在类别之间的转变概率。结果显示,无抑郁症状组的研究生保持在原组概率为84.1%,轻度抑郁症状组和中度抑郁症状组保持在原组的概率分别为56.9%和26.6%。无抑郁症状组向轻度抑郁症状组转变的概率为12.2%,轻度抑郁症状组向无抑郁症状组转变的概率为29.3%,中度抑郁症状组向轻度抑郁症状组转变的概率为41.3%。提示在研究生群体中,中度抑郁症状组的流动性最高。见表4。
在控制性别、年级、民族、生源地、是否独生子女变量后,以发展性困扰为预测变量,将研究生抑郁症状潜在类别的转变作为因变量,进行多项Logistic回归分析。结果显示,适应困难、就业压力和恋爱困扰能够显著预测抑郁症状潜在类别的转变。其中,适应困难是无抑郁症状组转变为轻度抑郁症状组的促进因素(OR=1.08,95%CI:1.02~1.16,P<0.05),就业压力是无抑郁症状组转变为轻度抑郁症状组的促进因素(OR=1.05,95%CI:1.01~1.10,P<0.05),恋爱困扰是中度抑郁症状组转变为无抑郁症状组的阻碍因素(OR=0.86,95%CI:0.76~0.99,P<0.05)。见表5。
结果显示,研究生抑郁症状存在群体异质性,可以划分为无抑郁症状组、轻度抑郁症状组和中度抑郁症状组3种类别。这与我国大学生群体抑郁分型的结果一致[24],不同之处在于各抑郁亚型的人数比例。研究生群体中无抑郁症状组占比约为65%,轻度抑郁症状组占比约为26%,中度抑郁症状组占比约为9%。超过1/3的研究生报告有抑郁症状,这与既往研究结果一致[3]。表明研究生整体心理健康状况良好,但仍有相当一部分学生面临抑郁症状的困扰,亟须引起足够重视。根据抑郁的素质-压力模型,抑郁症状的产生源于外部压力因素与内部心理素质的相互作用[25]。就研究生群体而言,其抑郁症状的形成原因具有鲜明的群体特异性。在压力源方面,研究生面临科研压力、就业压力、经济压力以及婚恋压力等多重挑战[26];在易感性素质方面,研究生普遍表现出较高的元认知水平和内省倾向。这种认知特征在适度压力下能够促进问题解决,但在过度压力情境中却容易激活反刍思维,从而诱发抑郁症状。因此,有必要重视研究生心理健康教育,构建研究生心理健康问题筛查与预警机制,以重点识别抑郁高危人群。
结果显示,研究生抑郁症状潜在类别的转变概率发生了显著变化。具体而言,无抑郁症状组最为稳定,轻度抑郁症状组表现出一定的流动性,中度抑郁症状组表现出较高的流动性。中度抑郁症状组的高流动性可能反映了干预的窗口期,提示该类别组研究生的抑郁症状在短期内更易发生变化。因此,应重点关注研究生抑郁症状的动态变化,尤其是针对中度抑郁症状组的个体。建议高校建立常态化的心理健康监测机制,定期开展心理测评,以便及时发现中度抑郁症状组的早期迹象。此外,对中度抑郁症状组应实施针对性的干预措施,例如提供专业心理咨询、情绪调节训练以及必要社会支持,以帮助其缓解抑郁情绪。
研究发现,研究生抑郁症状潜在类别在间隔1年的时间内呈现减缓趋势。具体表现为,中度抑郁症状组倾向转变为轻度抑郁症状组,轻度抑郁症状组倾向转变为无抑郁症状组。这一结果不同于大学生群体抑郁潜在类别的转变模式[13],即低抑郁症状组倾向转变为高抑郁症状组。究其原因,可能源于不同学段的学生在心理发展阶段、压力源性质以及社会支持系统等方面存在独特差异。首先,研究生群体处于成年早期成熟阶段,其认知功能水平、情绪调节能力及自我同一性发展相对稳固,因此面对压力时更容易激活以问题为中心的应对策略,从而促进抑郁症状向积极方向转化。其次,研究生面临的压力源通常具有结构性、目标导向性和时限性等特点,使得此类边界清晰的问题更易引发心理求助行为,从而降低研究生抑郁症状。第三,研究生依托导学关系和实验室同辈网络所形成的社会支持系统较为稳固,能够对压力源起到缓冲作用,进而抑制研究生抑郁症状的发展[27]。
结果显示,适应困难和就业压力是无抑郁症状组转变为轻度抑郁症状组的促进因素,而恋爱困扰是中度抑郁症状组转变为无抑郁症状组的阻碍因素。大量先前研究已表明,抑郁症状与压力事件之间存在显著关联[28-30]。本研究结果进一步支持了研究生抑郁症状与发展性困扰之间在纵向时间维度上的关联模式:随着研究生所经历的压力的增加,其抑郁症状愈加严重。根据心理社会发展理论,研究生群体正处于角色转换的关键期,需要同步完成自我身份建构、职业发展规划及亲密关系建立等多项发展任务。不良的学校适应可能会持续削弱研究生的自尊水平和社会成就感,从而激活其抑郁症状的认知易感性[31]。就业求职作为一种未来导向的慢性应激源,其不可控性与不确定性容易激发消极认知图式,使得研究生的自我评价降低,进而促使抑郁症状纵向聚集[32]。此外,恋爱困扰通过关系冲突削弱情感支持网络,导致研究生可获得的心理资源被耗散,从而显著降低缓冲效应,加速抑郁症状的恶化进程。鉴于此,建议高校将适应状况、就业压力和恋爱状况纳入研究生常态化心理健康筛查指标,并建立动态追踪机制,以便及时识别早期高风险个体,从而预防抑郁症状的发展。
本研究仍存在以下不足:首先,仅采用问卷调查法评定研究生抑郁症状,可能导致自主报告偏差。未来可考虑自评与他评相结合的方式多维评定抑郁症状;其次,本研究仅基于2年时间跨度的纵向数据,追踪时间的间隔较长,这在一定程度上限制了对研究生抑郁症状动态变化轨迹的深入探讨。未来研究可以缩短追踪时间的间隔,延长研究的追踪周期,并借助潜变量增长模型等统计技术,以进一步验证研究生抑郁症状转变规律的稳定性。
综上所述,本研究采用以个体为中心的分析方法,探究了研究生群体抑郁症状的潜在类别及其转变模式,并进一步揭示了发展性困扰对抑郁症状类别转变的影响。研究生抑郁症状可划分为无抑郁症状组、轻度抑郁症状组和中度抑郁症状组3种类别,其中后两类抑郁亚型的比例合计超过1/3。在1年间隔期内,研究生抑郁症状的潜在类别整体呈现减缓趋势。此外,适应困难、就业压力与恋爱困扰是影响研究生抑郁症状发展的重要因素。上述研究结果不仅有助于深入了解中国研究生抑郁症状的构成与特征,也为理解抑郁症状的动态变化规律提供了理论依据。

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2026年第48卷第2期
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doi: 10.16016/j.2097-0927.202508044
  • 接收时间:2025-08-16
  • 首发时间:2026-10-23
  • 出版时间:2026-01-30
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  • 收稿日期:2025-08-16
  • 修回日期:2025-11-08
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    1华中师范大学:大学生心理健康教育中心,湖北 武汉
    2华中师范大学:心理学院,湖北 武汉
    3湖北民族大学教师教育学院,湖北 恩施
    4云南农业大学心理健康教育与咨询中心,云南 昆明

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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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