Prevalence of mental health problems among college students in mainland China from 2010 to 2020: A meta-analysis

doi:10.3724/SP.J.1042.2022.00991

Abstract

Abstract:

College students are a huge part and an important member of the young people. Their physical and mental health not only directly affects their own long-term development, but also has a significant influence on the future of the nation. Therefore, the mental health of college students has become the focus of families, schools and even the whole society. In recent years, some studies have conducted meta-analyses on the prevalence of a certain mental health problem (such as depression and sleep problem) among college students in China, but such studies cannot reflect the mental health status of college students comprehensively. And some of the previous studies cannot reflect the current status of contemporary college students’ mental health. Furthermore, the selection of moderator variables was not comprehensive enough to reveal the key factors affecting the prevalence. Therefore, we employed a meta-analysis in this study to estimate the prevalence of typical mental health problems among college students in mainland China from 2010 to 2020, and the moderating effects of publication year, measurement tools and detection standards, detection time, region, birthplace, only child or not were further analyzed. For the selection of indicators, mental health problems were divided into two categories: internalizing problems and externalizing problems. The indicators of internalizing problems include anxiety, depression, sleep problem, somatization, and suicidal ideation, while the indicators of externalizing problems include nonsuicidal self-injury and suicide attempt.
Through the process of literature search and selection, 128 studies (136 effect sizes) of anxiety, 237 studies (244 effect sizes) of depression, 56 studies (58 effect sizes) of sleep problem, 49 studies (49 effect sizes) of somatization, 31 studies (31 effect sizes) of nonsuicidal self-injury, 51 studies (51 effect sizes) of suicide ideation, and 8 studies (8 effect sizes) of suicide attempt were included in this study. Homogeneity test indicated that random effects model was appropriate for the meta-analysis. The p-curve analysis illustrated no publication bias. Ultimately, the results of the main effect test showed that the prevalence rates of sleep problem, depression, nonsuicidal self-injury, anxiety, suicidal ideation, somatization, and suicide attempt were 23.5%, 20.8%, 16.2%, 13.7%, 10.8%, 4.5%, and 2.7%, respectively. The results indicate that internalizing problems, especially sleep problem and emotional problem, are more serious among Chinese college students compared to externalizing problems.
The results of the moderating effect indicated that (1) The prevalence of anxiety, depression, sleep problem and suicide attempt among college students has increased significantly in the last decade, while the prevalence of self-harm has declined significantly; (2) The prevalence of anxiety, depression, sleep problem, and somatization varied significantly between measurement tools and detection standards, and the prevalence of suicidal ideation differed to a significant extent depending on the detection time. Therefore, the fluctuation of prevalence was accounted by measurement tools, detection standards and detection time. (3) There existed obvious regional differences in the prevalence of sleep problem and suicidal ideation, with the feature of the worst mental health among college students in western China and better mental health among college students in northeastern and central China. (4) The prevalence of mental health problems among demographical variables including gender, only child or not, and birthplace showed no significant difference, which indicated that gender, only child or not, urban or rural areas were not the critical factors influencing college students’ mental health.
In summary, by employing the method of meta-analysis, this study is the first study to systematically investigate the prevalence of the typical mental health problems of college students in mainland China from 2010 to 2020. The results clarified the controversy over the inconsistent prevalence in previous studies and explored the main reasons for the inconsistent findings. Thus, this meta-analysis is conducive to promoting subsequent studies and educational practice.

Key words: mental health, college students, prevalence, meta-analysis

CHEN Yumeng, ZHANG Yali, YU Guoliang. Prevalence of mental health problems among college students in mainland China from 2010 to 2020: A meta-analysis[J]. Advances in Psychological Science, 2022, 30(5): 991-1004.

Figures/Tables 9

References 80

[1]	蔡群青, 袁振国, 贺文凯.(2021). 西部高等教育全面振兴的现实困境、逻辑要义与破解理路. 大学教育科学, 1, 26-35.
[2]	龚栩, 谢熹瑶, 徐蕊, 罗跃嘉.(2010). 抑郁-焦虑-压力量表简体中文版(DASS-21) 在中国大学生中的测试报告. 中国临床心理学杂志, 18(4), 443-446.
[3]	何安明, 夏艳雨.(2019). 手机成瘾对大学生认知失败的影响: 一个有调节的中介模型. 心理发展与教育, 35(3), 295-302.
[4]	林扬千.(2021). 精疲力竭的突围: 大学生内卷化现象的表现、危害及应对. 当代青年研究, 3, 88-93.
[5]	刘爱敏, 孙孟君.(2010). 理工科大学新生心理健康状况调查. 卫生研究, 39(1), 71-73.
[6]	刘福荣, 宋晓琴, 尚小平, 吴梦凡, 隋美丽, 董一超, 刘新奎.(2020). 中学生抑郁症状检出率的meta分析. 中国心理卫生杂志, 34(2), 123-128.
[7]	刘福荣, 吴梦凡, 董一超, 岳馨培, 尚小平, 隋美丽, 刘新奎.(2021). 小学生抑郁症状检出率的meta分析. 中国心理卫生杂志, 35(6), 482-488.
[8]	刘进, 林松月, 王艺蒙, 马丽娜.(2019). 西部生源大学生学业表现的多维对比分析-基于B大学N学院学生的实证研究. 重庆高教研究, 7(4), 12-29.
[9]	刘英慧, 张永红, 潘孝富.(2021). 中国研究生心理健康水平变迁的横断历史元分析. 内江师范学院学报, 36(6), 7-12+18.
[10]	潘珍, 毛绍菊, 唐寒梅, 傅燕艳, 孙玮璇, 廖志林,... 黄鹏.(2016). 中国大学生非自杀性自伤检出率的Meta分析. 中国学校卫生, 37(6), 878-881.
[11]	茹福霞, 黄秀萍, 詹文韵, 饶裕莲, 陈小龙, 胡旺, 黄鹏.(2019). 中国大陆大学生自杀计划检出率Meta分析. 中国学校卫生, 40(1), 42-45+50.
[12]	孙冉, 梁文艳.(2021). 第一代大学生身份是否会阻碍学生的生涯发展-基于首都大学生成长追踪调查的实证研究. 中国高教研究, 5, 43-49+108.
[13]	王纪申, 刘文理, 李强.(2021). 手机成瘾与大学生睡眠质量: 焦虑和反刍思维的作用. 中国临床心理学杂志, 29(5), 1060-1063+1068.
[14]	王蜜源, 韩芳芳, 刘佳, 黄凯琳, 彭红叶, 黄敏婷, 赵振海.(2020). 大学生抑郁症状检出率及相关因素的meta分析. 中国心理卫生杂志, 34(12), 1041-1047.
[15]	夏朝云, 王东波, 吴素琴, 叶剑辉.(2002). 自杀意念自评量表的初步制定. 临床精神医学杂志, 2, 100-102.
[16]	辛素飞, 王一鑫, 林崇德.(2018). 高职生心理健康水平变迁的横断历史研究: 1999-2016年. 教育研究, 39(11), 120-130.
[17]	辛自强, 张梅, 何琳.(2012). 大学生心理健康变迁的横断历史研究. 心理学报, 44(5), 664-679.
[18]	尹子臣, 胡韬.(2014). 西部大学生学习倦怠状况及影响因素分析. 新疆师范大学学报(哲学社会科学版), 35(5), 105-109.
[19]	俞国良, 王浩.(2020). 文化潮流与社会转型: 影响我国青少年心理健康状况的重要因素及现实策略. 西南民族大学学报(人文社科版), 41(9), 213-219.
[20]	俞国良, 赵凤青, 罗晓路.(2017). 心理健康教育:高等院校的地区差异比较研究. 黑龙江高教研究, 12, 89-92.
[21]	于增艳, 刘文.(2019). 智能手机使用与焦虑、抑郁和睡眠质量关系的meta分析. 中国心理卫生杂志, 33(12), 938- 943.
[22]	张亚利, 李森, 俞国良.(2019). 自尊与社交焦虑的关系: 基于中国学生群体的元分析. 心理科学进展, 27(6), 1005- 1018.
[23]	张亚利, 李森, 俞国良.(2021). 社交媒体使用与错失焦虑的关系: 一项元分析. 心理学报, 53(3), 273-290.
[24]	郑日昌, 邓丽芳, 张忠华, 郭召良.(2005). 《中国大学生心理健康量表》的编制. 心理与行为研究, 2, 102-108.
[25]	周少贤, 窦东徽.(2019). 社会变革下大学生情绪特点新趋势及应对--以北京市某高校为例. 中国青年社会科学, 38(4), 59-66.
[26]	Achenbach T. M.(1966). The classification of children’s psychiatric symptoms: A factor-analytic study. Psychological Monographs: General and Applied, 80(7), 1-37.
[27]	Baxter A. J., Scott K. M., Vos T., & Whiteford H. A.(2013). Global prevalence of anxiety disorders: A systematic review and meta-regression. Psychological Medicine, 43(5), 897-910. doi: 10.1017/S003329171200147X pmid: 22781489
[28]	Beck A. T., & Beamesderfer A.(1974). Assessment of depression: The depression inventory. Modern Problems of Pharmacopsychiatry, 7, 151-169. doi: 10.1159/000395074 pmid: 4412100
[29]	Beck A. T. & Steer R. A.(1990). Beck anxiety inventory manual. San Antonio, TX: The Psychological Corporation Harcourt Brace Jovanovich, Inc.
[30]	Beck A. T., Steer R. A., & Brown G. K.(1996). Manual for the Beck depression inventory. San Antonio, TX: Psychological Corporation.
[31]	Buysse D. J., Reynolds III C. F., Monk T. H., Berman S. R., & Kupfer D. J.(1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193-213. doi: 10.1016/0165-1781(89)90047-4 pmid: 2748771
[32]	Card N. A.(2012). Applied meta-analysis for social science research. New York: Guilford Press.
[33]	Chickering A. W., & Reisser L.(1993). Education and Identity. San Francisco: Jossey-Bass Inc.
[34]	Coplan R. J., & Bowker J. C.(Eds.). (2014). The handbook of solitude: Psychological perspectives on social isolation, social withdrawal, and being alone. West Sussex, UK: Wiley.
[35]	Cuijpers P., Cristea I. A., Ebert D. D., Koot H. M., Auerbach R. P., Bruffaerts R., & Kessler R. C.(2016). Psychological treatment of depression in college students: A metaanalysis. Depression and Anxiety, 33(5), 400-414. doi: 10.1002/da.22461 pmid: 26682536
[36]	Dadfar M., Asgharnejadfarid A. A., Hosseini A. F., Esfahani M. N., Lester D., & Kalibatseva Z.(2020). Measuring somatic symptoms with the PHQ-15: A comparative study of three Iranian samples. Mental Health, Religion & Culture, 23(3- 4), 289-301.
[37]	Derogatis L. R.(1977). SCL-90-R: Administration, scoring, and procedures manual I for the R(evised) version. Baltimore: Clinical Psychometric Research.
[38]	El-Gilany A. H., Amro M., Eladawi N., & Khalil M.(2019). Mental health status of medical students: A single faculty study in Egypt. Journal of Nervous and Mental Disease, 207(5), 348-354. doi: 10.1097/NMD.0000000000000970 URL
[39]	Flake J. K., & Fried E. I.(2020). Measurement schmeasurement: Questionable measurement practices and how to avoid them. Advances in Methods and Practices in Psychological Science, 3(4), 456-465. doi: 10.1177/2515245920952393 URL
[40]	Fried E. I.(2017). The 52 symptoms of major depression: Lack of content overlap among seven common depression scales. Journal of Affective Disorders, 208, 191-197. doi: S0165-0327(16)31312-X pmid: 27792962
[41]	Gao L., Xie Y. C., Jia C. H., & Wang W.(2020). Prevalence of depression among Chinese university students: A systematic review and meta-analysis. Scientific Reports, 10(1), Article 15897. https://doi.org/10.1038/s41598-020-72998-1
[42]	Gradisar M., Wolfson A. R., Harvey A. G., Hale L., Rosenberg R., & Czeisler C. A.(2013). The sleep and technology use of Americans: Findings from the National Sleep Foundation’s 2011 Sleep in America poll. Journal of Clinical Sleep Medicine, 9(12), 1291-1299. doi: 10.5664/jcsm.3272 pmid: 24340291
[43]	Guo L. P., Fan H. L., Xu Z., Li J. Y., Chen T. L., Zhang Z. Y., & Yang K. H.(2021). Prevalence and changes in depressive symptoms among postgraduate students: A systematic review and meta-analysis from 1980 to 2020. Stress and Health, 37(5), 835-847. doi: 10.1002/smi.3045 URL
[44]	Guo X. J., Meng Z., Huang G. F., Fan J. Y., Zhou W. W., Ling W. J., … Li S.(2016). Meta-analysis of the prevalence of anxiety disorders in mainland China from 2000 to 2015. Scientific Reports, 6(1), Article 28033. http://doi.org/10.1038/srep28033
[45]	Hirayama K.(2011). Japanese Association for College Mental Health. User guide for the UPI. [In Japanese] Tokyo: Sozo-shuppan.
[46]	Huedo-Medina T. B., Sánchez-Meca J., Marín-Martínez F., & Botella J.(2006). Assessing heterogeneity in meta- analysis: Q statistic or I² index? Psychological Methods, 11(2), 193-206. pmid: 16784338
[47]	Ibrahim A. K., Kelly S. J., Adams C. E., & Glazebrook C.(2013). A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research, 47(3), 391-400. doi: 10.1016/j.jpsychires.2012.11.015 pmid: 23260171
[48]	Kalkbrenner M. T., Jolley A. L., & Hays D. G.(2019). Faculty views on college student mental health: Implications for retention and student success. Journal of College Student Retention: Research, Theory & Practice, 23(3), 636-658.
[49]	Kroenke K., Spitzer R. L., & Williams J. B. W.(2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606-613. doi: 10.1046/j.1525-1497.2001.016009606.x pmid: 11556941
[50]	Lang J. J., & Yao Y. S.(2018). Prevalence of nonsuicidal self-injury in Chinese middle school and high school students: A meta-analysis. Medicine, 97, Article 42(e12916). http://dx.doi.org/10.1097/MD.0000000000012916
[51]	Li L., Wang Y. Y., Wang S. B., Zhang L., Li L., Xu D. D., … Xiang Y. T.(2018). Prevalence of sleep disturbances in Chinese university students: A comprehensive meta-analysis. Journal of Sleep Research, 27(3), Article e12648. http://doi.org/10.1111/jsr.12648
[52]	Li S. S., He J. N., Chen Q. Y., Zhu J. F., Zou H. D., & Xu X.(2018). Ocular surface health in Shanghai University students: A cross-sectional study. BMC Ophthalmology, 18(1), Article 245. http://doi.org/10.1186/s12886-018-0825-z
[53]	Li Z. Z., Li Y. M., Lei X. Y., Zhang D., Liu L., Tang S. Y., & Chen L. Z.(2014). Prevalence of suicidal ideation in Chinese college students: A meta-analysis. PloS One, 9(10), Article e104368. http://doi.org/10.1371/journal.pone.0104368
[54]	Liang M. J., Guo L., Huo J., & Zhou G. L.(2021). Prevalence of sleep disturbances in Chinese adolescents: A systematic review and meta-analysis. PloS One, 16(3), Article e0247333. https://doi.org/10.1371/journal.pone.0247333
[55]	Lin H., Harrist A. W., Lansford J. E., Pettit G. S., Bates J. E., & Dodge K. A.(2020). Adolescent social withdrawal, parental psychological control, and parental knowledge across seven years: A developmental cascade model. Journal of Adolescence, 81, 124-134. doi: 10.1016/j.adolescence.2020.04.007 URL
[56]	Lipson S. K., Lattie E. G., & Eisenberg D.(2019). Increased rates of mental health service utilization by U.S. college students: 10-year population-level trends (2007- 2017). Psychiatric Services, 70(1), 60-63. doi: 10.1176/appi.ps.201800332 URL
[57]	Lun K. W. C., Chan C. K., Ip P. K. Y., Ma S. Y. K., Tsai W. W., Wong C. S., … Yan D.(2018). Depression and anxiety among university students in Hong Kong. Hong Kong Medical Journal, 24(5), 466-472. doi: 10.12809/hkmj176915 pmid: 30245480
[58]	Mare R. D.(1980). Social background and school continuation decisions. Journal of the American Statistical Association, 75(370), 295-305. doi: 10.1080/01621459.1980.10477466 URL
[59]	Meng Q., Zhang J. W., Kang J., & Wu Y.(2020). Effects of sound environment on the sleep of college students in China. Science of The Total Environment, 705, Article 135794. https://doi.org/10.1016/j.scitotenv.2019.135794
[60]	Radloff L. S.(1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385-401. doi: 10.1177/014662167700100306 URL
[61]	Ridner S. L., Newton K. S., Staten R. R., Crawford T. N., & Hall L. A.(2016). Predictors of well-being among college students. Journal of American College Health, 64(2), 116-124. doi: 10.1080/07448481.2015.1085057 pmid: 26630580
[62]	Rothstein H. R., Sutton A. J., & Borenstein M.(2005). Publication bias in meta-analysis: Prevention, assessment and adjustments. Chichester: John Wiley & Sons Ltd.
[63]	Sa J., Choe C. S., Cho C. B. Y., Chaput J. P., Lee J., & Hwang S.(2020). Sex and racial/ethnic differences in suicidal consideration and suicide attempts among US college students, 2011-2015. American Journal of Health Behavior, 44(2), 214-231. doi: 10.5993/AJHB.44.2.9 URL
[64]	Salk R. H., Hyde J. S., & Abramson L. Y.(2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological Bulletin, 143(8), 783-822. doi: 10.1037/bul0000102 URL
[65]	Schlarb A. A., Claßen M., Hellmann S. M., Vögele C., & Gulewitsch M. D.(2017). Sleep and somatic complaints in university students. Journal of Pain Research, 10, 1189-1199. doi: 10.2147/JPR.S125421 URL
[66]	Shen Y. M., Meng F. C., Xu H. M., Li X., Zhang Y. R., Huang C. X., … Zhang X. Y.(2020). Internet addiction among college students in a Chinese population: Prevalence, correlates, and its relationship with suicide attempts. Depression and Anxiety, 37(8), 812-821. doi: 10.1002/da.23036 URL
[67]	Simonsohn U., Simmons J. P., & Nelson L. D.(2015). Better p-curves: Making p-curve analysis more robust to errors, fraud, and ambitious p-hacking, a reply to Ulrich and Miller (2015). Journal of Experimental Psychology: General, 144(6), 1146-1152. doi: 10.1037/xge0000104 URL
[68]	Soldatos C. R.(1995). The assessment of insomnia:Rationale for a new scale based on ICD-10 principles. In: W. Szelenberger, & A. Kukwa (Eds.), Sleep: Physiology and pathology. Warszawa: Elma Books.
[69]	Sullivan K., Zhu Q., Wang C., & Boyanton D.(2021). Relations among peer victimization, aggression, and school climate among elementary school students in China. School Psychology Review. Advance online publication. https://doi.org/10.1080/2372966X.2021.1898290
[70]	Tang X. F., Tang S. Q., Ren Z. H., & Wong D. F. K.(2019). Prevalence of depressive symptoms among adolescents in secondary school in mainland China: A systematic review and meta-analysis. Journal of Affective Disorders, 245, 498-507. doi: 10.1016/j.jad.2018.11.043 URL
[71]	Taouk M., Lovibond P. F., & Laube R.(2001). Psychometric properties of a Chinese version of the 21-item depression anxiety stress scales (DASS21). In Report for New South Wales Transcultural Mental Health Centre. Cumberland Hospital: Sydney.
[72]	Thomée S., Eklöf M., Gustafsson E., Nilsson R., & Hagberg M.(2007). Prevalence of perceived stress, symptoms of depression and sleep disturbances in relation to information and communication technology (ICT) use among young adults-An explorative prospective study. Computers in Human Behavior, 23(3), 1300-1321. doi: 10.1016/j.chb.2004.12.007 URL
[73]	Thurber C. A., & Walton E. A.(2012). Homesickness and adjustment in university students. Journal of American College Health, 60(5), 415-419. doi: 10.1080/07448481.2012.673520 URL
[74]	Twenge J. M.(2000). The age of anxiety? Birth cohort change in anxiety and neuroticism, 1952-1993. Journal of Personality and Social Psychology, 79(6), 1007-1021. pmid: 11138751
[75]	Wang F. F., & Bíró É.(2021). Determinants of sleep quality in college students: A literature review. Explore, 17(2), 170-177. doi: 10.1016/j.explore.2020.11.003 URL
[76]	Xin Z. Q., Zhang L., & Liu D.(2010). Birth cohort changes of Chinese adolescents’ anxiety: A cross-temporal meta-analysis, 1992-2005. Personality and Individual Differences, 48(2), 208-212. doi: 10.1016/j.paid.2009.10.010 URL
[77]	Xu D. D., Rao W. W., Cao X. L., Wen S. Y., An F. R., Che W. I., … Xiang Y. T.(2020). Prevalence of depressive symptoms in primary school students in China: A systematic review and meta-analysis. Journal of Affective Disorders, 268, 20-27. doi: 10.1016/j.jad.2020.02.034 URL
[78]	Yang L. S., Zhang Z. H., Sun L., Sun Y. H., & Ye D. Q.(2015). Prevalence of suicide attempts among college students in China: A meta-analysis. PloS One, 10(2), Article e0116303. https://doi.org/10.1371/journal.pone.0116303
[79]	Zung W. W.(1965). A self-rating depression scale. Archives of General Psychiatry, 12(1), 63-70. doi: 10.1001/archpsyc.1965.01720310065008 URL
[80]	Zung W. W.(1971). A rating instrument for anxiety disorders. Psychosomatics, 12(6), 371-379. pmid: 5172928

心理健康问题种类	k	被试数	效应值及95% 的置信区间			异质性检验
心理健康问题种类	k	被试数	检出率	下限	上限	I²	p
焦虑	136	209000	0.137	0.12	0.16	99.29	< 0.001
抑郁	244	407026	0.208	0.19	0.23	99.44	< 0.001
睡眠问题	58	137418	0.235	0.20	0.27	99.46	< 0.001
躯体化	49	83437	0.045	0.03	0.06	98.81	< 0.001
自杀意念	51	334070	0.108	0.10	0.12	99.15	< 0.001
自我伤害	31	126130	0.162	0.13	0.20	99.52	< 0.001
自杀未遂	8	36512	0.027	0.02	0.04	96.80	< 0.001

心理健康问题种类	k	被试数	效应值及95% 的置信区间			异质性检验
心理健康问题种类	k	被试数	检出率	下限	上限	I²	p
焦虑	136	209000	0.137	0.12	0.16	99.29	< 0.001
抑郁	244	407026	0.208	0.19	0.23	99.44	< 0.001
睡眠问题	58	137418	0.235	0.20	0.27	99.46	< 0.001
躯体化	49	83437	0.045	0.03	0.06	98.81	< 0.001
自杀意念	51	334070	0.108	0.10	0.12	99.15	< 0.001
自我伤害	31	126130	0.162	0.13	0.20	99.52	< 0.001
自杀未遂	8	36512	0.027	0.02	0.04	96.80	< 0.001

调节变量	异质性检验			类别	k	检出率	95% CI
调节变量	Q_B	df	p	类别	k	检出率	下限	上限
测量工具+检出标准	206.89	7	< 0.001	BAI (45)	3	0.067	0.03	0.13
				CCSMHS (68)	3	0.016	0.01	0.03
				DASS (8)	5	0.469	0.34	0.61
				SAS (>50)	4	0.148	0.09	0.25
				SAS (≥50)	58	0.181	0.16	0.21
				SCL-90(2)	21	0.170	0.14	0.21
				SCL-90(2.5)	3	0.026	0.01	0.05
				SCL-90(3)	20	0.039	0.03	0.05
检出时间	0.001	1	0.98	1周	124	0.141	0.12	0.16
检出时间	0.001	1	0.98	2周	3	0.142	0.06	0.31
性别	0.26	1	0.61	男生	45	0.151	0.12	0.19
性别	0.26	1	0.61	女生	45	0.138	0.11	0.17
独生与否	0.24	1	0.62	独生	14	0.205	0.14	0.29
独生与否	0.24	1	0.62	非独生	14	0.180	0.12	0.26
生源地	0.18	1	0.67	城镇	18	0.154	0.11	0.22
生源地	0.18	1	0.67	农村	18	0.171	0.12	0.24
区域	5.43	3	0.14	东北	9	0.108	0.06	0.19
				东部	41	0.154	0.12	0.20
				中部	36	0.159	0.12	0.21
				西部	30	0.106	0.08	0.14

调节变量	异质性检验			类别	k	检出率	95% CI
调节变量	Q_B	df	p	类别	k	检出率	下限	上限
测量工具+检出标准	206.89	7	< 0.001	BAI (45)	3	0.067	0.03	0.13
				CCSMHS (68)	3	0.016	0.01	0.03
				DASS (8)	5	0.469	0.34	0.61
				SAS (>50)	4	0.148	0.09	0.25
				SAS (≥50)	58	0.181	0.16	0.21
				SCL-90(2)	21	0.170	0.14	0.21
				SCL-90(2.5)	3	0.026	0.01	0.05
				SCL-90(3)	20	0.039	0.03	0.05
检出时间	0.001	1	0.98	1周	124	0.141	0.12	0.16
检出时间	0.001	1	0.98	2周	3	0.142	0.06	0.31
性别	0.26	1	0.61	男生	45	0.151	0.12	0.19
性别	0.26	1	0.61	女生	45	0.138	0.11	0.17
独生与否	0.24	1	0.62	独生	14	0.205	0.14	0.29
独生与否	0.24	1	0.62	非独生	14	0.180	0.12	0.26
生源地	0.18	1	0.67	城镇	18	0.154	0.11	0.22
生源地	0.18	1	0.67	农村	18	0.171	0.12	0.24
区域	5.43	3	0.14	东北	9	0.108	0.06	0.19
				东部	41	0.154	0.12	0.20
				中部	36	0.159	0.12	0.21
				西部	30	0.106	0.08	0.14

调节变量	异质性检验			类别	k	检出率	95% CI
调节变量	Q_B	df	p	类别	k	检出率	下限	上限
测量工具+检出标准	402.07	15	< 0.001	BDI-Ⅰ(14)	4	0.219	0.13	0.34
				BDI-Ⅰ(5)	3	0.459	0.30	0.63
				BDI-Ⅱ(14)	6	0.184	0.12	0.27
				CES-D (16)	19	0.435	0.37	0.50
				CES-D (20)	5	0.231	0.15	0.34
				DASS (10)	5	0.286	0.19	0.41
				PHQ-9(10)	3	0.126	0.07	0.22
				SCL-90(>2)	5	0.195	0.12	0.29
				SCL-90(≥2)	45	0.179	0.15	0.21
				SCL-90(2.5)	4	0.039	0.02	0.07
				SCL-90(3)	29	0.044	0.04	0.06
				SDS (>50)	4	0.213	0.13	0.33
				SDS (>53)	3	0.282	0.16	0.44
				SDS (≥50)	36	0.322	0.28	0.37
				SDS (≥53)	41	0.282	0.25	0.32
				CCSMHS(69)	3	0.050	0.03	0.10
检出时间	0.16	1	0.69	1周	213	0.209	0.19	0.23
检出时间	0.16	1	0.69	2周	22	0.222	0.17	0.29
性别	0.10	1	0.75	男生	90	0.258	0.23	0.29
性别	0.10	1	0.75	女生	90	0.250	0.22	0.28
独生与否	0.51	1	0.48	独生	31	0.318	0.28	0.36
独生与否	0.51	1	0.48	非独生	31	0.297	0.26	0.34
生源地	0.02	1	0.90	城镇	41	0.280	0.24	0.32
生源地	0.02	1	0.90	农村	41	0.284	0.25	0.33
区域	2.81	3	0.42	东北	17	0.190	0.13	0.26
				东部	71	0.191	0.16	0.22
				中部	60	0.231	0.19	0.27
				西部	62	0.207	0.17	0.25