E-cigarette Marketing Regulations and Youth Vaping: Cross-Sectional Surveys, 2017–2019
Bibliographic record
Abstract
BACKGROUND: Increased electronic cigarette (e-cigarette) use among young people is often attributed to industry marketing practices; however, the effectiveness of regulations that limit e-cigarette advertising and promotions has yet to be examined. New federal legislation that liberalized the Canadian e-cigarette market in May 2018, along with differences in provincial regulations, provides an opportunity to examine the impact of regulatory restrictions on e-cigarette marketing. METHODS: Repeat cross-sectional surveys of 12 004 16- to 19-year-olds in Canada, completed online in 2017, 2018, and 2019. Logistic regression models were used to examine differences over time in exposure to e-cigarette marketing and e-cigarette use, including between provinces with differing strengths of marketing restrictions. RESULTS: The percentage of youth surveyed who reported noticing e-cigarette promotions often or very often approximately doubled between 2017 and 2019 (13.6% vs 26.0%; adjusted odds ratio [AOR] = 2.24, 95% confidence interval [CI] = 1.97–2.56). Overall exposure to marketing was generally more prevalent in provinces with fewer regulatory restrictions. Respondents who reported noticing marketing often or very often were more likely to report vaping in the past 30 days (AOR = 1.41, 95% CI = 1.23–1.62), past week (AOR = 1.44, 95% CI = 1.22–1.70), and ≥20 days in the past month (AOR = 1.42, 95% CI = 1.11–1.81, P = .005). Provinces with low restrictions on marketing had higher prevalence of vaping in the past 30 days (AOR = 1.50, 95% CI = 1.25–1.80, P < .001), and in the past week (AOR = 1.65, 95% CI = 1.33–2.05, P < .001). CONCLUSIONS: Exposure to marketing and the prevalence of vaping increased among Canadian youth after the liberalization of the e-cigarette market in 2018. Comprehensive provincial restrictions on e-cigarette marketing were associated with lower levels of exposure to marketing and lower prevalence of e-cigarette use.
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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 | 0.001 | 0.001 |
| 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".