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Introduction: The literature indicates that pandemics significantly impact the mental health of frontline health workers. While the effects of COVID-19 on the mental health of frontline nurses have been studied, their lived experiences remain insufficiently explored. Objective: This study aims to investigate the lived experience of nurses who were deployed to support Wuhan during the COVID-19 pandemic. Methods: This study adopted a qualitative study design. A purposive sample of fifteen nurses were recruited from a group of nurses who supported Wuhan during COVID-19 pandemic. The data was collected during May and June 2020. Data collection occurred in May and June 2020, employing semistructured interviews conducted via telephone. Interpretative phenomenological analysis (IPA) was utilized to analyze the collected data by two independent researchers. This report follows the COREQ checklist. Results: Frontline nurses supporting Wuhan likened their experience to being on a battlefield. Four superordinate themes emerged: (1) mobilization for combating COVID-19; (2) rapid adaption to a dynamic high-stress environment; (3) navigating psychological distress; and (4) the journey home. Conclusion: This study offers comprehensive insights into the lived experience of nurses deployed from other provinces to assist COVID patients in Wuhan. The findings indicate that despite facing a variety of challenges, these frontline nurses were capable of rapid adaptation and successfully fulfilled their roles. Recommendations for future preparedness in public health emergencies are provided. Additionally, follow-up research is warranted to explore the long-term effects of frontline experience on the mental health of the nurses and their family members. [ABSTRACT FROM AUTHOR]

University students are experiencing a mental health crisis. COVID-19 has exacerbated this situation. We have surveyed students in 2 universities in Lebanon to gauge their mental health challenges. We have constructed a machine learning (ML) approach to predict symptoms of depression, anxiety, and stress based on demographics and self-rated health measures. Our approach involved developing 8 ML predictive models, including Logistic Regression (LR), multi-layer perceptron (MLP) neural network, support vector machine (SVM), random forest (RF) and XGBoost, AdaBoost, Naïve Bayes (NB), and K-Nearest neighbors (KNN). Following their construction, we compared their respective performances. Our evaluation shows that RF (AUC = 78.27%), NB (AUC = 76.37%), and AdaBoost (AUC = 72.96%) have provided the highest-performing AUC scores for depression, anxiety, and stress, respectively. Self-rated health is found to be the top feature in predicting depression, while age was the top feature in predicting anxiety and stress, followed by self-rated health. Future work will focus on using data augmentation approaches and extending to multi-class anxiety predictions. [ABSTRACT FROM AUTHOR]

The aftermath effects including overstressed health systems, increased number of cases, death rates, and patient aggression have significant implications on the healthcare workers' psychological well-being. Building on both the Health Belief Model and Conservation of Resources theory, this study examined the impact of patient aggression on healthcare workers' psychological well-being, anxiety, and mental health during the COVID-19 pandemic. Further, the study tested the mediating role of psychological well-being in the abovementioned relationships. Data obtained from 549 Lebanese healthcare workers in private hospitals in a two-wave survey was used to evaluate the proposed study's relationships, using confirmatory factor analysis and structural equation modeling. The results indicated that patient aggression has significant negative impact on healthcare workers' psychological well-being and mental health while it increases anxiety. Further, psychological well-being partially mediated the relationship between patient aggression and anxiety. Theoretical contributions, practical implications of the study, and suggestions for further studies are discussed. [ABSTRACT FROM AUTHOR]