Investigating anxiety levels in the Quebec university community during the COVID-19 pandemic using machine learning and data exploration techniques

Numerous studies have demonstrated the adverse impact of the COVID-19 global pandemic on the mental health of post-secondary students worldwide. However, there have been relatively few investigations conducted in a university setting that encompasses both students and employees. Furthermore, almost all previous studies relied on conventional statistical analysis. Our research specifically examined anxiety levels among the Quebec university community during the COVID-19 pandemic, with a focus on the Generalized Anxiety Disorder (GAD-7) score using both conventional data exploration and predictive machine learning techniques. We found that the CatBoost algorithm was the best predictor of the GAD-7 score, achieving a squared Pearson correlation coefficient of r^2=0.5656 . Additionally, we utilized SHapley Additive exPlanations (SHAP) to explore variable importance and interaction effects between the variables in the predictive model. Finally, we also examined anxiety levels from the perspective of a classification task. Unfortunately, the results obtained were not as satisfactory as those obtained by the regression approach.