Predicting depression and anxiety of Chinese population during COVID-19 in psychological evaluation data by XGBoost.

BACKGROUND: Due to the onset of sudden stress, COVID-19 has greatly impacted the incidence of depression and anxiety. However, challenges still exist in identifying high-risk groups for depression and anxiety during COVID-19. Studies have identified how resilience and social support can be employed as effective predictors of depression and anxiety. This study aims to select the best combination of variables from measures of resilience, social support, and alexithymia for predicting depression and anxiety. METHODS: The eXtreme Gradient Boosting (XGBoost1) model was applied to a dataset including data on 29,841 participants that was collected during the COVID-19 pandemic. Discriminant analyses on groups of participants with depression (DE2), anxiety (AN3), comorbid depression and anxiety (DA4), and healthy controls (HC5), were performed. All variables were selected according to their importance for classification. Further, analyses were performed with selected features to determine the best variable combination. RESULTS: The mean accuracies achieved by three classification tasks, DE vs HC, AN vs HC, and DA vs HC, were 0.78, 0.77, and 0.89. Further, the combination of 19 selected features almost exhibited the same performance as all 56 variables (accuracies = 0.75, 0.75, and 0.86). CONCLUSIONS: Resilience, social support, and some demographic data can accurately distinguish DE, AN, and DA from HC. The results can be used to inform screening practices for depression and anxiety. Additionally, the model performance of a limited scale including only 19 features indicates that using a simplified scale is feasible.

Pathways from self-disclosure to medical coping strategy among adolescents with moderate and major depression during the COVID-19 pandemic: A mediation of self-efficacy

Background The prevalence of adolescent depression in China during the COVID-19 pandemic is increasing. Self-disclosing depressive emotions could help release stress. Self-disclosure, which is a prerequisite for self-efficacy, can directly contribute to people’s psychological health, and depression and the choice of coping strategy are determined by the level of self-efficacy perceived. Purpose We aimed to discuss the relationship between self-efficacy, self-disclosure, and medical coping strategy. Further, we explore the mediation effect of self-efficacy on the influence of self-disclosure on medical coping strategies in adolescents with depression. Methods A total of 585 patients aged 11–24 years with moderate and major depression were recruited. All the assessments were completed on the second day after admission, including the General Self-Efficacy Scale (GSE), Distress Disclosure Index (DDI), and Medical Coping Modes Questionnaire (MCMQ). Pearson correlation was performed to explore the relationships of these variables. The bootstrap analysis was used to conduct to assess the mediation effects. Results Both direct and indirect effects of self-disclosure on medical coping strategy were found. As predicted, self-efficacy partially mediated the relationship between self-disclosure and medical coping strategy (b = 0.0385, 95% CI: 0.0244–0.0538 for Confrontation;b = –0.0466, 95%CI: –0.0651 to –0.0296 for Resignation), respectively. The effect size for Confrontation and Resignation was 0.2659 and 0.2485, respectively. Conclusion Self-efficacy played a partial mediating role in the effect of self-disclosure on medical coping strategies for adolescent depression during the COVID-19 pandemic, and the use of a positive self-disclosure mechanism may be anticipated to promote improved self-efficacy and the use of active coping strategies.