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: This was a quasi-experimental study with university students as participants. Intervention group participants were instructed to complete online questionnaires which covered basic demographics and instruments assessing depression, anxiety, stress, mindfulness, psychological flexibility, and fear of COVID-19 before and after the one-hour intervention. The control group also completed before and after questionnaires and were subsequently crossed over to the intervention group. Repeated measures ANOVA was conducted to assess time*group effects.
RESULTS: 118 participants were involved in this study. There were significant differences in anxiety (F(1,116) = 34.361, p < 0.001, partial eta-squared = 0.229) and psychological flexibility between the two groups (F(1,116) = 11.010, p = 0.001, partial eta-squared = 0.087), while there were no differences in depression, stress, mindfulness, or fear of COVID-19.
CONCLUSION: The results of this study corroborate the efficacy of online single-session mindfulness therapy as a viable short-term psychological intervention under financial and time constraints. Since university students are in the age group with the highest incidence of depressive and anxiety disorders, it is crucial to utilize resources to address as many students as possible to ensure maximum benefit.
METHOD: Through an online survey, we used Coronavirus Anxiety Scale (CAS) to measure the level of anxiety associated with the COVID-19 crisis and Brief Coping Orientation to Problems Experienced (COPE) to assess the coping responses adopted to handle stressful life events. Coping strategies were classified as adaptive and maladaptive, for which the aggregate sores were calculated. Multiple linear regression was used to determine the predictors of anxiety adjusted for potentially confounding variables. Results from 434 participants were available for analysis.
RESULTS: The mean score (SD) of the CAS was 1.1 (1.8). The mean scores of adaptive and maladaptive coping strategies were 35.69 and 19.28, respectively. Multiple linear regression revealed that maladaptive coping [Adjusted B coefficient = 4.106, p-value < 0.001] and presence of comorbidities [Adjusted B coefficient = 1.376, p-value = 0.025] significantly predicted anxiety.
CONCLUSION: Maladaptive coping and presence of comorbidities were the predictors of coronavirus anxiety. The apparent lack of anxiety in relation to COVID-19 and movement restriction is reflective of the reported high level of satisfaction with the support and services provided during the COVID-19 outbreak in Malaysia. Adaptive coping strategies were adopted more frequently than maladaptive. Nevertheless, public education on positive coping strategies and anxiety management may be still be relevant to provide mental health support to address the needs of the general population.
METHODS: University students who were studying in a Malaysia university with a mean age of 24.0 years (n = 380; females 71.6%) were recruited through convenience sampling between 19 August and 30 September 2021. They completed a Google Form consisting of information on sociodemographic background, weight stigma, psychological distress and self-reported body weight and height. Psychometric testing was conducted using the classical test theory (including confirmatory factor analysis) and Rasch models to confirm the two-factor structure of WSSQ and the unidimensional structure of the PWS using the various fit indices. Concurrent validity of the total scores of WSSQ and PWS with psychological distress and body mass index (BMI) was also investigated. Internal consistency using Cronbach's alpha was conducted.
RESULTS: The confirmatory factor analyses and Rasch analyses verified the two-factor structure for the WSSQ and the single-factor structure for the PWS. Both the WSSQ and PWS showed good internal consistency and good concurrent validity as demonstrated by their significant correlations with psychological distress and BMI.
CONCLUSION: The WSSQ and PWS have strong validity and reliability, and they can both be used to assess weight stigma among Malaysian university students.
LEVEL OF EVIDENCE: V: Descriptive study.
AIMS: This study aims to evaluate the effect of theory- and web-based health education intervention on mental health literacy among foundation students at a public university in Malaysia.
METHODS: A randomised controlled trial study will be conducted among foundation students. Participants will be recruited and randomly assigned to either the intervention or control group. The intervention will be conducted for two weeks with a one-month follow-up. The health education intervention will be developed according to the Information, Motivation, and Behavioural Skill Theory, and will be delivered via a website. The outcome will be measured using validated, self-administered questionnaires. at baseline, post-intervention, and one-month follow up. The data will be analysed using Generalised Estimating Equation (GEE). This study is registered to the Thai Clinical Trial Registry (TCTR) (reference number: TCTR20210705006), dated 4th July 2021.
CONCLUSIONS: The results from this study will be useful for relevant authorities to take further efforts in mental health promotion among young people.
MATERIALS AND METHODS: A total of 480 students from different faculties in a Malaysian public university participated in this study. They were selected by simple random sampling method. They completed self-administered questionnaires including the Malay Version of Internet Addiction Test (MVIAT)) to measure internet addiction and Adult Self-Report Scale (ASRS) Symptom Checklist, Depression Anxiety Stress Scales (DASS) and UCLA Loneliness Scale (Version 3) to assess for ADHD symptoms, depression, anxiety, stress, and loneliness respectively.
RESULTS: The prevalence of IA among university students was 33.33% (n = 160). The respondents' mean age was 21.01 ± 1.29 years old and they were predominantly females (73.1%) and Malays (59.4%). Binary logistic regression showed that gender (p = 0.002; OR = 0.463, CI = 0.284-0.754), ADHD inattention (p = 0.003; OR = 2.063, CI = 1.273-3.345), ADHD hyperactivity (p<0.0001; OR = 2.427, CI = 1.495-3.939), stress (p = 0.048; OR = 1.795, CI = 1.004-3.210) and loneliness (p = 0.022; OR = 1.741, CI = 1.084-2.794) were significantly associated with IA.
CONCLUSION: A third of university students had IA. In addition, we found that those who were at risk of IA were males, with ADHD symptoms of inattention and hyperactivity, who reported stress and loneliness. Preventive strategy to curb internet addiction and its negative sequelae may consider these factors in its development and implementation.
MATERIALS AND METHODS: This study was conducted by distribution of a questionnaire developed as part of the Global Youth Tobacco Survey to Form 4 student in 3 schools at Shah Alam.
RESULTS: Prevalence of smoking (current smokers) was 7.5%. Almost half of the children came from families where one or both parents smoked and a third of the parents had no discussion regarding consequences of smoking with them. A large number of students were classified as "triers" as they had tried smoking and were unsure of whether they would not be smoking in the future. Contrary to our expectations, students generally felt smoking did make one feel more uncomfortable and helped one to reduce body weight. Most students seemed to be aware of the ill-effects of smoking on health. They felt they had received adequate information from school regarding the effects on smoking on health.
CONCLUSIONS: Our study showed that even though Form 4 students in Shah Alam were knowledgeable about ill-effects of smoking and were taught so as part of their school curriculum, the prevalence of smoking was still high. Students in the "trier group" represent a potential group of future smokers and strategies targeting tobacco control may be aimed at tackling these vulnerable individuals. Efforts are also needed to help educate secondary school children about common misconceptions and dispel myths associated with cigarette smoking.