OBJECTIVES: To investigate the prevalence of suicidal ideation and its factors in first-year Chinese university students from a vocational college in Zhejiang during the COVID-19 pandemic.
METHODS: Using a cluster sampling technique, a university-wide survey was conducted of 686 first-year university students from Hangzhou in March 2020 using University Personality Inventory (UPI). UPI includes an assessment for suicidal ideation and possible risk factors. Suicidal ideation prevalence was calculated for males and females. Univariate analysis and multivariable logistic regression models were conducted, adjusting for age and sex. Analyses were carried out using the SPSS version 22.0 software.
RESULTS: The prevalence of 12-month suicidal ideation among first-year university students during March 2020 was 5.2%, and there was no significant difference between males and females (4.8% vs. 6.0%, x2 = 0.28, p = 0.597). Multivariable logistic regression analysis identified social avoidance (B = 0.78, OR = 2.17, p < 0.001) and emotional vulnerability (B = 0.71, OR = 2.02, p < 0.001) as positively associated with suicidal ideation.
CONCLUSIONS: Social avoidance and emotional vulnerabilities are unique factors associated with greater suicidal ideation among first-year university students during the COVID-19 pandemic. UPI serves as a validated tool to screen suicide risks among Chinese university students. Encouraging social engagement and improving emotional regulation skills are promising targets to reduce suicidal ideation among first-year university students.
MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.
RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.
CONCLUSION: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.
METHODS: Participants comprised 1912 college students (16-28 years old, 47.2% female) from three universities in Jilin Province, China, who completed the self-report assessments of psychological strains (40 items Psychological Strains Scale) and suicidal behaviors (Suicidal Behaviors Questionnaire-Revised). The demographic characteristics included four variables: health status, psychological status, academic status and economic status.
RESULTS: Approximately 15.0% (286/1912) of participants were classified as having suicide risk, based on the cut-off scores of the SBQ-R. The prevalence of suicidal behaviors among males and females was 11.9% (120/1009) and 18.4% (166/903), respectively. Value strain (OR = 1.075, 95%CI: 1.057-1.094), aspiration strain (OR = 1.082, 95%CI: 1.064-1.101), deprivation strain (OR = 1.073, 95%CI: 1.052-1.093), and coping strain (OR = 1.095, 95%CI: 1.075-1.116) were risk factors for suicidality in college students. Coping strain (OR = 1.050, 95%CI: 1.023-1.077) was still positively associated with suicide risk in multivariate logistic regression. Logistic regression analysis indicated that coping strain had the highest correlation with suicidal behaviors.
LIMITATIONS: The directionality of the relationships cannot be deduced because this study is cross-sectional.
CONCLUSION: This study confirms a strong association between psychological strains and suicidal behaviors in college students. Some measures can be taken to reduce psychological strains to mitigate suicide risk among college students. More studies investigating coping strain among college students are warranted.
METHOD: The sample consisted of 520 first-year undergraduate international students. The experimental group contained students who were diagnosed with depression and homesickness and received seven sessions of brief individual CBT for depression to reduce homesickness. The control group contained students who were diagnosed with depression and homesickness and received one session of advice and suggestions. The comparison group contained students who experienced only homesickness and did not receive any interventions. The study used the comparison group to determine if an interaction effect existed between students experiencing only homesickness and students experiencing both homesickness and depression.
RESULTS: Students who received brief individual CBT displayed a significant reduction in their homesickness and depression scores compared to the scores of students in the control group. Students who experienced only homesickness exhibited a significant reduction in the scores on homesickness in the post-assessment compared to the control group's post-assessment homesickness scores.
LIMITATION: The results of this study cannot be generalized as data were collected from three universities in Malaysia. The follow-up assessment was conducted six months after the post-assessment, which also limits generalizability beyond six months.
CONCLUSION: Overall, homesickness is considered a normal reaction. Brief individual CBT for depression is effective in reducing homesickness and depression among international students.
DESIGN AND MEASURES: Data were analysed from the Global School-Based Student Health Survey Timor-Leste (n = 3455). An ordered probit model was used to assess the effects of demographic, lifestyle, social, and psychological factors on different levels of worry-related sleep problems (i.e., no, mild and severe sleep problems).
RESULTS: School-going adolescents were more likely to face mild or severe worry-related sleep problems if they were older, passive smokers, alcohol drinkers and moderately active. School-going adolescents who sometimes or always went hungry were more likely to experience worry-related sleep problems than those who did not. Involvement in physical fights, being bullied, and loneliness were positively associated with the probability of having modest or severe worry-related sleep problems.
CONCLUSION: Age, exposure to second-hand smoke, alcohol consumption, physical activity, going hungry, physical fights, being bullied and loneliness are the important determining factors of adolescent worry-related sleep problems. Policymakers should pay special attention to these factors when formulating intervention measures.