<b>Trends in exposure to and perceptions of e-cigarette marketing among youth in England, Canada and the United States between 2017 and 2019</b>
Bibliographic record
Abstract
E-Cigarette marketing may influence e-cigarette use among youth. This study examined reported exposure to and perceptions of e-cigarette marketing among youth between 2017 and 2019 across countries with varying e-cigarette marketing restrictions. Cross-sectional online surveys were conducted with 35 490 youth aged 16-19 from England, Canada and the United States in 2017, 2018 and 2019. Weighted logistic regression models examined trends in the adjusted prevalence of self-reported exposure to e-cigarette marketing and the perceived appeal of e-cigarette ads between 2017 and 2019, by country and by smoking/vaping status. Reports of frequent exposure to e-cigarette marketing increased between 2017 and 2019 in all countries, but less so in England, where e-cigarette marketing is more restricted. Perceiving e-cigarette marketing as appealing increased from 2017 to 2019 in Canada and the United States, but not in England. In England, exposure to e-cigarette marketing did not increase in prohibited channels between 2017 and 2019. Between 2017 and 2019, never-users' reports increased for exposure to and appeal of e-cigarette marketing. The results suggest some effectiveness of e-cigarette marketing bans in England and receptivity to e-cigarette marketing among youth never users.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".