Cannabis Use Among Adults in Cigarette Smoking Cessation Treatment in Ontario, Canada: Prevalence and Association With Tobacco Cessation Outcome, 2015–2021
Bibliographic record
Abstract
Objectives. To examine cannabis use prevalence and its association with tobacco cessation among adults enrolled in cigarette smoking cessation treatment before and after Canada legalized recreational cannabis in October 2018. Methods. The sample comprised 83 206 adults enrolled in primary care–based cigarette smoking cessation treatment between 2015 and 2021 in Ontario, Canada. Past-30-day cannabis use was self-reported at enrollment and cigarette smoking abstinence at 6-month follow-up. Results. Past-30-day prevalence of cannabis use increased from 20.2% in 2015 to 37.7% in 2021. The prevalence increased linearly both before and after legalization. Cannabis and tobacco co-use was associated with lower odds of self-reported cigarette smoking abstinence at 6-month follow-up than tobacco use only (24.4% vs 29.3%; odds ratio [OR] = 0.78; 95% confidence interval [CI] = 0.75, 0.81). This association was attenuated after adjustment for covariates (OR = 0.93; 95% CI = 0.89, 0.97) and weakened slightly over time. Conclusions. Cannabis use prevalence almost doubled from 2015 to 2021 among primary care patients in Ontario seeking treatment to quit cigarettes and was associated with poorer quit outcomes. Further research into the impact of cannabis policy on cannabis and tobacco co-use is warranted to mitigate harm. (Am J Public Health. 2024;114(1):98–107. https://doi.org/10.2105/AJPH.2023.307445 )
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".