ABSTRACT
Many studies showed that COVID-19 global pandemic had a negative impact on the mental health of post-secondary students over the world. To date, very few studies have been conducted in a university setting, not only with students but also with employees. Moreover, almost all studies were based on classical statistical analysis. In this study, we investigated the level of anxiety felt by the Quebec university community (students and employees) during COVID-19 pandemic. Especially, we focused on the generalized anxiety disorder (GAD-7) score with the help of classical data exploration and predictive machine learning techniques. We observed that the best predictive model of the GAD-7 score was provided by the CatBoost algorithm) reaching a squared Pearson correlation coefficient of r2 = 0.5656. Moreover, we also explored variable importance and interaction effects between variables involved in the predictive model obtained using SHapley Additive exPlanations (SHAP). © 2022 ACM.