The Co‐Use of Tobacco and Cannabis Among Adolescents Over a 30‐Year Period
Bibliographic record
Abstract
BACKGROUND: This study explores the patterns of use and co-use of tobacco and cannabis among Ontario adolescents over 3 decades and if characteristics of co-users and single substance users have changed. METHODS: Co-use trends for 1981-2011 were analyzed using the Centre for Addiction and Mental Health Ontario Student Drug Use and Health Survey, which includes 38,331 students in grades 7, 9, and 11. A co-user was defined as someone reporting daily tobacco and/or cannabis use in the past month. Trends over time (by gender and academic performance) were analyzed with logistic regression. RESULTS: The prevalence of tobacco-only use, cannabis-only use, and co-use fluctuated considerably. During 1981-1993, there were more tobacco-only users than co-users and cannabis-only users; since 1993 the prevalence of tobacco use has decreased dramatically. Co-use prevalence peaked at 12% (95% confidence interval: 9, 15) in 1999, when prevalence of overall use of both substances was highest. In 2011, 92% of tobacco users also used cannabis, up from 16% in 1991. CONCLUSIONS: In 2011 nearly all students who smoke tobacco daily also use cannabis. Non-regular use of either substance is highest now compared with the past 3 decades. Contemporary tobacco and cannabis co-users are significantly different than past users. Youth prevention programs should understand the changing context of cannabis and tobacco among youth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".