Social norms towards smoking and vaping and associations with product use among youth in England, Canada, and the US
Bibliographic record
Abstract
BACKGROUND: This study assesses differences in social norms towards smoking and vaping among youth across countries (England, Canada, US) and smoking and vaping status. METHODS: Data are from the 2017 ITC Youth Tobacco and Vaping Survey, among youth age 16-19 in England (N = 3444), Canada (N = 3327), and US (N = 3509). Prevalence of friend smoking, friend vaping, peer approval of smoking, and peer approval of vaping were estimated. Adjusted logistic regression models were estimated for each norm to assess associations with country, smoking status, and vaping status, adjusting for sociodemographics, alcohol use, and marijuana use. RESULTS: 47% and 52% reported friend smoking and vaping respectively. Perceived peer approval of vaping (44%) was almost double that of smoking (23%). Compared with England, fewer Canadian and US youth reported friend smoking (Canada: AOR = 0.71 [95% CI = 0.62-0.82]; US: AOR = 0.54 [0.47-0.62]) and peer approval of smoking (Canada: AOR = 0.74 [0.63-0.87]; US: AOR = 0.78 [0.67-0.91]), yet more reported peer approval of vaping (Canada: AOR = 1.23 [1.08-1.41]; US: AOR = 1.30 [1.14-1.48]). More Canadian than English youth reported friend vaping (AOR = 1.17 [1.02-1.36]). Friend smoking, peer approval of smoking, and friend vaping were more common among smokers and vapers (all p < .02). Peer approval of vaping was more common among vapers but less common among smokers (all p < .044). CONCLUSIONS: Youth had more positive vaping than smoking norms. English youth reported the most pro-smoking but least pro-vaping norms in adjusted models; this was unexpected given country differences in regulatory environments. Norms towards both products were associated with use, with some evidence of cross-product associations between norms and behaviours.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".