Psychological Disorders and Coping among Undergraduate College Students: Advocating for Students' Counselling Services at Kuwait University.

Objectives: The objectives of the current study are twofold. First, it aimed to explore the prevalence of depression, anxiety and stress symptoms (i.e., psychological disorders) among Kuwait University students. Second, it sought to identify and quantify the associated risk factors as well as the students' coping strategies utilized to address these psychological disorders. Methods: We used a cross-sectional study at Kuwait University and selected students using a multistage stratified cluster sampling design among the 15 faculties of Kuwait University. To serve the study purposes, two cross-cultural and validated instruments were used, including the Depression, Anxiety and Stress Scale 21 (DASS-21) and the Brief-COPE scale. Descriptive statistics, as well as logistic regression analysis, were used to analyze the study findings. Results: A sample of 1142 students from various faculties participated in this study. We found that 681 (59.6%), 791 (69.3%) and 588 (51.5%) of the participants had depression, anxiety and stress symptoms, respectively. The highest coping strategies for stressors and challenges faced were moderate and high emotion-based coping strategies (n = 1063, 93.1%). Students from the Faculty of Allied Health Sciences as well as students from the Faculty of Engineering had significantly higher stress levels compared with students from other faculties (p < 0.05). Our results demonstrated that family problems were consistently a significant predictor of depression, anxiety and stress symptoms among Kuwait University students (p < 0.05). We further found that students who presented with stress and anxiety symptoms and those who practiced avoidant-focused coping strategies were substantially more likely to experience depression (ORs ≥ 2.7, p < 0.01). Conclusions: Our findings inferred that the majority of Kuwait University students have a remarkably high prevalence of mental health problems, mainly anxiety, depression, and stress symptoms along with inconsistent coping strategies toward the faced challenges during their studies. Therefore, the most important recommendation of the current study is the establishment of counselling centers in all faculties at Kuwait University. In turn, doing so facilitates the integration of wellness programs and the provision of comprehensive educational seminars, specialized training sessions and self-management techniques for Kuwait University students, leading to desired academic outcomes.

Anxiety disorders among children and adolescents during COVID-19 lockdowns and school closures: a cross-sectional study in Kuwait.

Introduction: Investigating the epidemiology of mental health disorders resulting from COVID-19 intervention measures, primary school closures, and social isolation in children and adolescents needs to be prioritized over adults at the post-pandemic stage. Most preliminary psychosocial studies conducted during the pandemic have demonstrated that younger age groups are the most vulnerable to such implications. Thus, this study aims to estimate the probable prevalence of specific anxiety disorders in children and quantify their relationships with relevant demographic risk factors. Methods: We used a cross-sectional study comprising 430 children aged between 8- and 18 years old living in Kuwait during the period of school closures as well as full and partial lockdowns. The survey included questions about participants' characteristics, children's anxiety using the Screen for Child Anxiety Related Emotional Disorders Questionnaire (SCARED) scale, and children's emotions and behaviours using the Strengths and Difficulties Questionnaire (SDQ). Univariate and multivariate logistic regression analyses were used to summarize the demographic and characteristics of the participants and their association with general, social, and generalized anxieties, as well as behavioural and emotional difficulties. Results: We inferred that 24.83% of our participants had at least one anxiety disorder, while 20.19% were classified as abnormal on the SDQ scale. Our multivariate analysis revealed that lockdown duration and sex of the child were consistently significant predictors (p-values < 0.05) of the broad spectrum of selected mental disorders. Additionally, we inferred notable increases in the likelihood of mental disorders associated with the increased duration of lockdowns. Conclusions: Our findings revealed preliminary insights into the vulnerability of young populations to the indirect negative impacts of strict public health measures during pandemic emergencies. Thus, authorities should consider such implications when planning and implementing similar interventions in future pandemics.

Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait.

The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.