Substance use classes and symptoms of anxiety and depression among Canadian secondary school students
Bibliographic record
Abstract
INTRODUCTION: Few studies have assessed patterns of substance use among Canadian adolescents. This cross-sectional study examined substance use classes among Canadian secondary school students and associations with anxiety and depression. METHODS: This study used data from Year 6 (2017/18) of the COMPASS study. Students (n = 51 767) reported their substance use (alcohol, cannabis, cigarette and e-cigarette use) and anxiety and depression symptoms. We employed latent class analysis to identify substance use classes and multinomial logistic regression to examine how anxiety and depression were associated with class membership. RESULTS: Overall, 40% of students indicated having anxiety and/or depression (50% in females; 29% in males) and 60% of students reported substance use (60% in females; 61% in males). We identified three substance use classes: poly-use, dual use, and non-use. Females with both anxiety and depression had the highest odds of being in the poly-use class compared to the non-use class (odds ratio [OR] = 4.09; 95% confidence interval [CI]: 3.59-4.65) followed by females with depression only (OR = 2.65; 95% CI: 2.31-3.04) and males with both anxiety and depression (OR = 2.48; 95% CI: 2.19-2.80). Symptomatology was also associated with belonging to the dual use class except among males with anxiety only (OR = 1.13; 95% CI: 0.94-1.37). CONCLUSION: Canadian secondary school students are engaging in dual and poly-substance use, and anxiety and depression were associated with such use. Females had a higher prevalence of anxiety and depression and should be a priority population for mental health programming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".