Extent and correlates of self-reported exposure to tobacco advertising, promotion and sponsorship in smokers: Findings from the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
INTRODUCTION: Tobacco advertising, promotion, and sponsorship (TAPS) are known to promote tobacco consumption and to discourage smoking cessation. Consequently, comprehensive TAPS bans are effective measures to reduce smoking. The objective of this study was to investigate to what extent smokers are exposed to TAPS in general, and in various media and localities, in different European countries. METHODS: A Cross-sectional analysis of national representative samples of adult smokers in 2016 from Germany, Greece, Hungary, Poland, Romania, and Spain (EUREST-PLUS Project, n=6,011), as well as England (n=3,503) and the Netherlands (n=1,213) (ITC Europe Surveys) was conducted. Prevalence of self-reported TAPS exposure is reported by country, and socio-economic correlates were investigated using logistic regression models. RESULTS: Self-reported exposure to TAPS varied widely among the countries, from 15.4 % in Hungary to 69.2 % in the Netherlands. In most countries, tobacco advertising was most commonly seen at the point of sale, and rarely noticed in mass media. The multivariate analysis revealed some variation in exposure to TAPS by sociodemographic factors. Age showed the greatest consistency across countries with younger smokers (18-24-year-olds) being more likely to notice TAPS than older smokers. CONCLUSIONS: TAPS exposure tended to be higher in countries with less restrictive regulation but was also reported in countries with more comprehensive bans, although at lower levels. The findings indicate the need for a comprehensive ban on TAPS to avoid a shift of marketing efforts to less regulated channels, and for stronger enforcement of existing bans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".