Cross-country comparison of cigarette and vaping product marketing exposure and use: findings from 2016 ITC Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
OBJECTIVE: To compare exposure to and use of certain cigarette and vaping product marketing among adult smokers and vapers in four countries with contrasting regulations-Australia (AU), Canada, England and the USA. DATA SOURCES: Adult smokers and vapers (n=12 294) from the 2016 International Tobacco Control (ITC) Four Country Smoking and Vaping Survey (4CV1). ANALYSIS: Self-reported exposure to cigarette and vaping product advertising through point-of-sale, websites/social media, emails/texts, as well as exposure to and use of price offers were assessed for country differences using logistic regression models adjusted for multiple covariates. RESULTS: Reported exposure to cigarette advertising exposure at point-of-sale was higher in the USA (52.1%) than in AU, Canada and England (10.5%-18.5%). Exposure to cigarette advertising on websites/social media and emails/texts was low overall (1.5%-10.4%). Reported exposure to vaping ads at point-of-sale was higher in England (49.3%) and USA (45.9%) than in Canada (32.5%), but vaping ad exposure on websites/social media in Canada (15.1%) was similar with England (18.4%) and the USA (12.1%). Exposure to vaping ads via emails/texts was low overall (3.1%-9.9%). Exposure to, and use of, cigarette price offers was highest in the USA (34.0 % and 17.8 %, respectively), but the use rate among those exposed was highest in AU (64.9%). Exposure to, and use of, price offers for vaping products was higher in the USA (42.3 % and 21.7 %) than in AU, Canada and England (25.9%-31.5 % and 7.4%-10.3 %). CONCLUSIONS: Patterns of cigarette and vaping product marketing exposure generally reflected country-specific policies, except for online vaping ads. Implications for research and policy are discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".