The scale and duration of the worldwide SARS-COVID-2 virus-related quarantine measures presented the global scientific community with a unique opportunity to study the accompanying psychological stress. Since March 2020, numerous publications have reported similar findings from diverse international studies on psychological stress, depression, and anxiety, which have increased during this pandemic. However, there remains a gap in interpreting the results from one country to another despite the global rise in mental health problems. The objective of our study was to identify global indicators of pandemic-related stress that traverse geographic and cultural boundaries. We amalgamated data from two independent global surveys across twelve countries and spanning four continents collected during the first wave of the mandated public health measures aimed at mitigating COVID-19. We applied machine learning (ML) modelling to these data, and the results revealed a significant positive correlation between PSS-10 scores and gender, relationship status, and groups. Confinement, fear of contagion, social isolation, financial hardship, etc., may be some reasons reported being the cause of the drastic increase in mental health problems worldwide. The decline of the typical protective factors (e.g., sleep, exercise, meditation) may have amplified existing vulnerabilities/co-morbidities (e.g., psychiatric history, age, gender). Our results further show that ML is an apropos tool to elucidate the underlying predictive factors in large, complex, heterogeneous datasets without invalidating the model assumptions. We believe our model provides clinicians, researchers, and decision-makers with evidence to investigate the moderators and mediators of stress and introduce novel interventions to mitigate the long-term effects of the COVID-19 pandemic.
Prediction of Mental Health Problems among Higher Education Student Using Machine Learning
