JMIR Publications

ABSTRACT

Background:

Mental health challenges are prevalent among Canadian higher-education students, with significant rates of depression and anxiety often going untreated due to lack of awareness, stigmatizing beliefs, and practical barriers. The U-Flourish longitudinal survey study launched in 2018 at Queen’s University, repeatedly collecting self-reported data on student mental health and well-being.

Objective:

Repeated U-Flourish survey data provide a unique opportunity to train an evidence-based recommendation engine to signpost students to rationalized mental health support based on their individual data.

Objective:

Repeated U-Flourish survey data provide a unique opportunity to train an evidence-based recommendation engine to signpost students to rationalized mental health support based on their individual data.

Methods:

Two approaches were used and integrated in developing this recommendation engine: (i) clinically-defined rules by experts in the field which signpost students to indicated levels of care based on clinical factors (i.e., self-harm and suicidal thoughts, symptom levels, lifetime history) following a stepped care approach; (ii) predictive Machine Learning (ML) models, trained with additional data including family history, early adversity, and stress indicators, to predict future risks of clinically significant depression (PHQ-9) and anxiety (GAD-7). Models were created using the XGBoost algorithm, and a 70:30 ratio for training and testing with 10-fold cross-validation.

Results:

Nearly one-third of students were signposted to seek assessment through various levels of university mental health services based on the clinically-defined rules. Optimizing thresholds to reduce false negatives, the ML models predicted worsening anxiety and depression over the year with results comparable to clinical screening (sensitivity equal to or above 90% for all models trained). Individuals considered to be at risk were signposted primarily to self-guided resources for proactive prevention. Model findings also demonstrated that abbreviated screens (PHQ-2, GAD-2), with potential to reduce respondent burden and improve feasibility, can be used without compromising sensitivity. SHAP analyses revealed key predictive factors such as early trauma, family history of mental illness, and functional impairment associated with depression and anxiety symptoms.

Conclusions:

The system’s dual approach optimizes resource allocation and promotes targeted early intervention and prevention, potentially improving capacity to address the increasing burden on university mental health services. Future directions include independent validation at other institutions, co-design, and validation including framing of AI-powered personalized recommendations with student users to improve accessibility and outcomes.