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Background: The postgraduate entrance examination can be a milestone for many medical students to advance their careers. An increasing number of students are competing for limited postgraduate offers available, and failure to enter postgraduate studies can have adverse mental health consequences. In this paper, we aim to investigate the mental health status of medical students during the postgraduate application entrance examination and to provide a targeted basis for mental health education and psychological counselling. Methods: Using the Symptom Checklist-90 scale (SCL-90) questionnaire, the mental health status of 613 students who passed two rounds of the Postgraduate Entrance Examination in 2019 to enroll in Guangzhou Medical University in China was evaluated and followed up for retesting 6 months later. We used SPSS 20.0 statistical software for comparative analysis, including One-Sample T-Test, Independent-Samples T-Test, Paired Samples T-Test and Chi-square Test. Results: Our data showed that 12.10% of students had mental health problems during the postgraduate entrance examination, and it decreased significantly to 4.40% at the 6-month follow-up after the examination period finished (P < 0.01). Somatization was the most significant symptom of the students both during and after the postgraduate entrance examination stages. All SCL-90 factors were scored significantly lower both in and after the postgraduate entrance examination stages than the 2008 national college student norm score (P < 0.01). Excluding psychiatric factors, all other SCL-90 factors in the postgraduate entrance examination stage scored higher than the graduate stage (P < 0.05), and the total score of SCL-90 in female medical students was higher compared to male students (P < 0.05). Conclusion: The postgraduate entrance examination event has a significant negative influence on students' mental health. The mental health of college and graduate students as an important part of their higher education experience should be systematically studied, and psychological counselling or help should be provided to them throughout their studies, specifically during the examination period. Educating applicants about mental health should be implemented during the postgraduate entrance examination curriculum. [ABSTRACT FROM AUTHOR]

With global aging, the number of elderly with physical disabilities is also increasing. Compared with the ordinary elderly, the elderly who lose their independence are more likely to have the symptoms of depression. Reducing depression may help to alleviate the disability process of those who find themselves in the disabled stages. Therefore, the purpose of this study is to explore the predictive effects of demographic characteristics, health behavior, health status, family relations, social relations, and subjective attitude on depression in rural and urban disabled elderly to improve early depression symptom recognition. A total of 1460 older adults aged 60 and disabled were selected from China Family Panel Studies (CFPS). Depression was assessed according to The Center for Epidemiologic Studies Depression Scale (CES-D). This paper used the random forest classifier to predict the depression of the disabled elderly from six aspects: demographic characteristics, health status, health behavior, family relationship, and social relationship. The prediction model was established based on 70% of the training set and 30% of the test set. The depression rate of rural disabled elderly was 57.67%, and that of urban disabled elderly was 44.59%. The mean values of the 10-k cross-validated results were 0.71 in rural areas and 0.70 in urban areas. AUC:0.71, specificity: 65.3%, sensitivity: 80.6% for rural disabled elderly with depression; AUC:0.78, specificity: 78.1%, sensitivity: 64.2% for urban disabled elderly with depression, respectively. There are apparent differences in the top ten predictors between rural and urban disabled elderly. The common predictors were self-rated health, changing in perceived health, disease or accidence experience within the past 2 weeks, life satisfaction, trusting people, BMI, and having trust in the future. Non-common predictors were chronic diseases, neighborly relations, total medical expenses within 1 year, community emotion, sleep duration, and family per capita income. Using random forest data to predict the depression of the disabled elderly may lead to early detection of depression. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]