Daily heavy and binge vaping is associated with higher alcohol and cannabis co-use
Bibliographic record
Abstract
The associations between vaping in young people and alcohol and cannabis co-use remain understudied. The current study examined the effect of vaping frequency on past 30-day alcohol and cannabis use. Using an online survey, regular vapers (N = 1328, aged 16–24) from Canada responded to a demographic and vaping questionnaire and provided information regarding e-cigarette use and alcohol and cannabis co-use. A k-means cluster analysis was used to segment users based on vaping frequency, and a one-way MANOVA tested vaper cluster membership effects on past 30-day alcohol and cannabis use. Pairwise comparisons measured specific mean differences, and crosstabulation with Bonferroni tests examined demographic differences among clusters. Vaper cluster membership had a significant effect on past 30-day alcohol and cannabis use. Daily heavy and binge vapers had higher rates of past 30-day alcohol and cannabis use. Non-daily light vapers were less likely to share their vape and more likely to have never owned a vape. Non-daily light vapers were less likely to use high nicotine concentrations. High vaping frequency places its users at risk for higher alcohol and cannabis use and high-risk vaping behavior. Nicotine caps, among other policies, may be key in reducing high vaping frequency and its negative consequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".