Daily heavy and binge vaping is associated with higher alcohol and cannabis co-use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it