背景:由于学业压力,社会关系,以及适应独立生活的变化,大学生承受着巨大的压力。因此,研究大学生的心理健康问题非常重要。建立一个可以检测大学生心理健康风险预警信号的预测模型,有助于支持早期干预和提高整体幸福感。
目的:调查大学生目前的心理幸福感,找出导致其下降的因素,并构建预测列线图模型。
方法:分析湖北省部分高校40874名大学生的心理健康状况,中国2022年3月1日至15日,采用在线问卷调查和随机抽样。影响他们心理健康的因素也使用逻辑回归方法进行了分析,采用R4.2.3软件建立风险预测的列线图模型。
结果:我们随机抽取了918个有效数据,发现11.3%的大学生存在心理问题。一般资料调查结果显示,博士生心理健康问题比专科生突出,农村学生的心理健康比城市学生更容易出现异常。此外,经历过重大生活事件和离异父母的学生更有可能有异常状态。异常组患者健康问卷-9(PHQ-9)和广泛性焦虑症-7评分明显高于健康组,差异有统计学意义(P<0.05)。通过多变量分析得出的列线图预测模型包括六个预测因子:原产地,不管他们是独生子女,是否有重大的生活事件,父母的婚姻状况,定期锻炼,亲密的朋友,和PHQ-9评分。训练集显示受试者工作特征(ROC)曲线下面积(AUC)为0.972[95%置信区间(CI):0.947-0.997],特异性0.888和灵敏度0.972。同样,验证集的ROCAUC为0.979(95CI:0.955-1.000),特异性为0.942,灵敏度为0.939。H-L偏差检验结果为χ2=32.476,P=0.000007,表明模型校准良好。
结论:在这项研究中,近11.3%的当代大学生存在心理问题,风险因素包括来自农村地区的学生,离异的父母,非单身儿童,不经常锻炼,和重大的生活事件。
BACKGROUND: Due to academic pressure, social relations, and the change of adapting to independent life,
college students are under high levels of pressure. Therefore, it is very important to study the mental health problems of
college students. Developing a predictive model that can detect early warning signals of
college students\' mental health risks can help support early intervention and improve overall well-being.
OBJECTIVE: To investigate
college students\' present psychological well-being, identify the contributing factors to its decline, and construct a predictive nomogram model.
METHODS: We analyzed the psychological health status of 40874 university students in selected universities in Hubei Province,
China from March 1 to 15, 2022, using online questionnaires and random sampling. Factors influencing their mental health were also analyzed using the logistic regression approach, and R4.2.3 software was employed to develop a nomogram model for risk prediction.
RESULTS: We randomly selected 918 valid data and found that 11.3% of college students had psychological problems. The results of the general data survey showed that the mental health problems of doctoral students were more prominent than those of junior college students, and the mental health of students from rural areas was more likely to be abnormal than that of urban students. In addition, students who had experienced significant life events and divorced parents were more likely to have an abnormal status. The abnormal group exhibited significantly higher Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 scores than the healthy group, with these differences being statistically significant (P < 0.05). The nomogram prediction model drawn by multivariate analysis included six predictors: The place of origin, whether they were single children, whether there were significant life events, parents\' marital status, regular exercise, intimate friends, and the PHQ-9 score. The training set demonstrated an area under the receiver operating characteristic (ROC) curve (AUC) of 0.972 [95% confidence interval (CI): 0.947-0.997], a specificity of 0.888 and a sensitivity of 0.972. Similarly, the validation set had a ROC AUC of 0.979 (95%CI: 0.955-1.000), with a specificity of 0.942 and a sensitivity of 0.939. The H-L deviation test result was χ2 = 32.476, P = 0.000007, suggesting that the model calibration was good.
CONCLUSIONS: In this study, nearly 11.3% of contemporary college students had psychological problems, the risk factors include students from rural areas, divorced parents, non-single children, infrequent exercise, and significant life events.