Impact of Point-of-Sale Tobacco Display Bans in Thailand: Findings from the International Tobacco Control (ITC) Southeast Asia Survey
Bibliographic record
Abstract
In September 2005 Thailand became the first Asian country to implement a complete ban on the display of cigarettes and other tobacco products at point-of-sale (POS). This paper examined the impact of the POS tobacco display ban in Thailand, with Malaysia (which did not impose bans) serving as a comparison. The data came from the International Tobacco Control Southeast Asia Survey (2005-2011), a prospective cohort survey designed to evaluate the psychosocial and behavioral impacts of tobacco control policies. Main measures included smokers' reported awareness of tobacco displays and advertising at POS. At the first post-ban survey wave over 90% of smokers in Thailand were aware of the display ban policy and supported it, and about three quarters thought the ban was effective. Noticing tobacco displays in stores was lowest (16.9%) at the first post-ban survey wave, but increased at later survey waves; however, the levels were consistently lower than those in Malaysia. Similarly, exposure to POS tobacco advertising was lower in Thailand. The display ban has reduced exposure to tobacco marketing at POS. The trend toward increased noticing is likely at least in part due to some increase in violations of the display bans and/or strategies to circumvent them.
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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 | 0.001 |
| 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".