Impact of point-of-sale tobacco display bans: findings from the International Tobacco Control Four Country Survey
Bibliographic record
Abstract
This study examined the impact of point-of-sale (POS) tobacco marketing restrictions in Australia and Canada, in relation to the United Kingdom and the United States where there were no such restrictions during the study period (2006-10). The data came from the International Tobacco Control Four Country Survey, a prospective multi-country cohort survey of adult smokers. In jurisdictions where POS display bans were implemented, smokers' reported exposure to tobacco marketing declined markedly. From 2006 to 2010, in Canada, the percentages noticing POS tobacco displays declined from 74.1 to 6.1% [adjusted odds ratio (OR) = 0.26, P < 0.001]; and reported exposure to POS tobacco advertising decreased from 40.3 to 14.1% (adjusted OR = 0.61, P < 0.001). Similarly, in Australia, noticing of POS displays decreased from 73.9 to 42.9%. In contrast, exposure to POS marketing in the United States and United Kingdom remained high during this period. In parallel, there were declines in reported exposures to other forms of advertising/promotion in Canada and Australia, but again, not in the United States or United Kingdom. Impulse purchasing of cigarettes was lower in places that enacted POS display bans. These findings indicate that implementing POS tobacco display bans does result in lower exposure to tobacco marketing and less frequent impulse purchasing of cigarettes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".