Patterns of anxiety, depression, and substance use risk behaviors among university students in Canada
Bibliographic record
Abstract
Objective: To identify subgroups of students with distinct profiles of mental health symptoms (MH) and substance use risk (SU) and the extent to which MH history and socio-demographics predict subgroup membership. Participants: University students (N = 10,935: 63% female). Methods: Repeated cross-sectional survey administered weekly to stratified random samples. Latent class analysis (LCA) was used to identify subgroups and multinomial regression was used to examine associations with variables of interest. Results: LCA identified an optimal 4-latent class solution: High MH–Low SU (47%), Low MH–Low SU (22%), High MH–High SU (19%), and Low MH–High SU (12%). MH history, gender, and ethnicity were associated with membership in the classes with high risk of MH, SU, or both. Conclusion: A substantial proportion of students presented with MH, SU, or both. Gender, ethnicity and MH history is associated with specific patterns of MH and SU, offering potentially useful information to tailor early interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".