Predictive Models for Canadian Healthcare Workers Mental Health During COVID-19.

Abstract

Purpose: COVID-19 impact on the population's mental health has been reported worldwide. Predicting healthcare workers' mental health and life stress is needed to proactively plan for future emergencies.

Design: Statistics Canada has surveyed Canadian healthcare workers and those working in healthcare settings to gauge their perceived mental health and perceived life stress.

Setting: A cross-sectional survey of healthcare workers in Canada.

Subjects: A sample of 18,139 healthcare workers respondents.

Analysis: Eight algorithms, including Logistic Regression, Random Forest (RF), Naive Bayes (NB), K Nearest Neighbours (KNN), Adaptive boost (AdaBoost), Multi-layer perceptron (MLP), XGBoost, and LightBoost. AUC scores, accuracy and precision were measured for all models.

Results: XGBoost provided the highest performing model AUC score (AUC = 82.07%) for predicting perceived mental health, and Random Forest performed the best for predicting perceived life stress (AUC = 77.74%). Perceived health, age group of participants, and perceived mental health compared to before the pandemic were found to be the most important 3 features to predict perceived mental health and perceived stress. Perceived mental health compared to before the pandemic was the most important predictor for perceived life stress.

Conclusion: Our models are highly predictive of healthcare workers' perceived mental health and life stress. Implementing scalable, non-expensive virtual mental health solutions to address mental health challenges in the workplace could mitigate the impact of workplace conditions on healthcare workers' mental health.