Development of a Recommendation Engine to University Student Mental Health Support Aligned With Stepped Care: Longitudinal Cohort Study

The risk prediction models and recommendation engine's dual approach rationalize support allocation and promote targeted early intervention and prevention, potentially improving capacity to address the increasing burden on university mental health services. Future directions include further refinement based on a larger harmonized and enriched dataset, independent validation, and implementation studies to estimate the complex factors that influence uptake, reach to services, and acceptability across more diverse student users.