Trends in the co-occurrence of substance use and mental health symptomatology in a national sample of US post-secondary students from 2009 to 2019
Bibliographic record
Abstract
Objective: This study examined joint trends over time in associations between substance use (heavy drinking, cannabis, and cigarette smoking) and mental health concerns (depression, anxiety, and suicidal ideation) among US post-secondary students. Participants: Data came from 323,896 students participating in the Healthy Minds Study from 2009 to 2019, a national cross-sectional survey of US post-secondary students. Weighted two-level logistic regression models with a time by substance interaction term were used to predict mental health status. Results: Use of each substance was associated with a greater odds of students endorsing depression, anxiety, and suicidal ideation. Over time, the association with mental health concerns strengthened substantially for cannabis, modestly for heavy drinking, and remained stable for smoking. Conclusion: Given co-occurrence is common and increasing among post-secondary students, college and university health systems should prioritize early identification, psychoeducation, harm-reduction, and brief interventions to support students at risk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".