Reported exposure to E-cigarette advertising and promotion in different regulatory environments: findings from the International Tobacco Control Four Country (ITC-4C) Survey
Bibliographic record
Abstract
© 2018 Electronic cigarette (e-cigarette) advertising regulations differ across countries. This study examines how differences in e-cigarette advertising regulations influence exposure to e-cigarette advertising, and perceptions about what participants had seen and read about e-cigarettes. Data come from the ITC Four Country Survey (Canada [CA], United States [US], Australia [AU] and United Kingdom [UK]) carried out between August 2013 and March 2015 (n = 3460). In 2014, AU and CA had laws prohibiting the retail sale of e-cigarettes containing nicotine while the US and UK had no restrictions, although a voluntary agreement restricting advertising in the UK was introduced during fieldwork. Smokers and ex-smokers were asked whether in the last six months they had noticed e-cigarettes advertisements and received free samples/special offers (promotion), and about their perceptions (positive or otherwise) of what they had seen or read about e-cigarettes. Data were analyzed in 2017. US and UK participants were more likely to report that they had noticed e-cigarette advertisements and received promotions compared to CA or AU participants. For TV and radio advertisements, reported exposure was higher in US compared to UK. For all types of advertisements, reported exposure was higher in CA than AU. Overall, nearly half of AU (44.0%) and UK (47.8%) participants perceived everything they had seen and read about e-cigarettes to be positive, with no significant differences between AU and UK. Participants in countries with permissive e-cigarette advertising restrictions and less restrictive e-cigarette regulations were more likely to notice advertisements than participants in countries with more restrictive e-cigarette regulations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".