Co-use and Mixing Tobacco With Cannabis Among Ontario Adults
Bibliographic record
Abstract
INTRODUCTION: The legalization of nonmedical cannabis in 2018 may have important implications for tobacco use in Canada. There is a risk of renormalizing tobacco use with co-use of tobacco and cannabis introducing nontobacco users to tobacco. Co-use is the use of both substances by the same individual at the same time or on different occasions, as well as mixed together. This study assessed the prevalence of co-use and mixing of tobacco and cannabis among Ontario adults and the characteristics of the users. AIMS AND METHODS: Data from the 1996 to 2017 cycles of the Centre for Addiction and Mental Health Monitor (n = 4481) were used to examine trends in prevalence and the proportion of Ontario adults co-using and mixing tobacco and cannabis. Logistic regression was used to study associations between user characteristics and co-use and mixing. RESULTS: Co-use of cigarettes and cannabis among cannabis users declined from 59.8% in 1996 to 41.7% in 2017. Past-year e-cigarette use was the only predictor of co-use. From 2015 to 2017, 31.1% (95% confidence interval 27.0, 35.9) of Ontario adults who used cannabis reported mixing it with tobacco in the past year. Being white, past-year e-cigarette use, having moderate or high nicotine dependence, and having moderate or high risk for cannabis problems were significant predictors of mixing among cannabis users. CONCLUSION: Given the well-established negative health effects associated with tobacco use, alongside a growing evidence base for negative health effects of cannabis smoking, co-use and mixing could pose a considerable public health concern in the context of legalization. IMPLICATIONS: Considerable effort has been expended to reduce tobacco smoking. However, current efforts to reduce tobacco smoking may be diminished since this study found the prevalence of mixing tobacco and cannabis among cannabis users in Ontario to be higher than expected. Mixing tobacco and cannabis may introduce nontobacco smokers to tobacco, exposing them to health risks associated with both cannabis and tobacco smoke. Therefore, there is a need to monitor changes in tobacco use and understanding implications for tobacco control and cessation programs within the changing environment of cannabis legalization in Canada and other jurisdictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".