Trends of poly-substance use among Canadian youth
Bibliographic record
Abstract
Poly-substance use, increasingly understood as a behaviour with uniquely adverse consequences, is on the rise among Canadian youth. High levels of e-cigarette vaping and the recent legalization of recreational cannabis use may result in an acceleration of this trend. The aim of this work was to characterise changes in youth poly-substance use over time, generate baseline data for future investigations, and highlight areas of interest for policy action. Descriptive statistics and regression models explored patterns and trends in concurrent use of multiple substances (alcohol, cigarettes, cannabis, and e-cigarettes) among Canadian high school students taking part in the COMPASS prospective cohort study during Y2 (2013/2014; n = 45,298), Y3 (2014/2015, n = 42,355), Y4 (2015/2016; n = 40,436), Y5 (2016/2017; n = 37,060), and Y6 (2017/2018; n = 34,879). Poly-substance use increased significantly over time, with over 50% of students who used substance reporting past-year use of multiple substances by 2017/2018. Male and Indigenous students were significantly more likely to report poly-substance use than female and white students respectively. E-cigarette vaping doubled from Y5 to Y6 and was included in all increasingly prevalent substance use combinations. Youth poly-substance use, rising since 2012/2013, saw a particularly steep increase after 2016/2017. Differential effects were observed for distinct demographic subpopulations, indicating tailored interventions may be required. E-cigarette vaping surged in parallel with the observed increase, suggesting a key role for this behaviour in shaping youth poly-substance use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".