Modes of Delivery in Concurrent Nicotine and Cannabis Use (“Co-Use”) among Youth: Findings from the International Tobacco Control (Itc) Survey
Bibliographic record
Abstract
Background Cannabis use is more common among nicotine users than non-users. This study characterized concurrent use of nicotine and cannabis (“co-use”) among 12,064 youth aged 16–19 years residing in Canada, the United States, and England in 2017. Methods Data were from the ITC Youth Tobacco & Vaping Survey (Wave 1). Seven modes of cannabis delivery (MOD) were characterized by country of residence and past 30-day use of combusted tobacco and electronic cigarettes. Weighted multivariable regression models were fitted to assess the correlates of co-use and each cannabis MOD. Results Seventy percent of cannabis users reported nicotine use. Co-users exhibited behavioral and demographic differences compared to exclusive users of either substance. “Smoking cannabis without tobacco” was the most popular form of use (78%). Use of nicotine-containing e-cigarettes was associated with “using an e-cigarette to vape cannabis oil/liquid” (aOR: 4.96, 95%CI: 2.23–11.06). Combustible tobacco use was associated with “smoking cannabis with tobacco in a joint/blunt” (aOR: 2.93, 95%CI: 1.89–4.56). Country-level differences were detected. Conclusions Nicotine use is substantial among cannabis users, and associations exist between modes of delivery for both drugs. Results underscore the importance of studying cannabis and nicotine use concurrently, and the need to address the use of both substances in developing interventions for youth users.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".