Alcohol Marketing and Consumption in Thailand: Results from the International Alcohol Control Policy Study
Bibliographic record
Abstract
Background: Alcohol marketing is a facilitator of alcohol consumption and related harm. The objectives of this study were to examine associations between alcohol consumption and exposure to and liking of alcohol marketing activities in Thailand. Methods: Data were obtained from the Thailand International Alcohol Control Policy study in 2012/2013 with 5,808 respondents aged betwee 15 and 65 years. Logistic regression models were applied to determine factors associated with liking alcohol advertisements and being a current drinker, regular drinker and binge drinker. Results: Of all respondents, 75% were exposed to alcohol advertising on television followed by sports sponsorship (69%) and point of sale (66%). Youth reported higher levels of exposure to alcohol advertising via all activities/channels, particularly online media, than adults (except radio). Respondents with high exposure to alcohol advertising were more likely to like alcohol advertising (adjusted odds ratio (AOR)=7.32, 95%confidence interval (CI): 4.91-10.92), compared to respondents who never exposed to alcohol advertising. The odds ratios of being a drinker (AOR=2.28, 95%CI: 1.82-2.85), a regular drinker (2.10, 1.57-2.81) and a binge drinker (2.57, 1.94-3.41) were significantly higher among those who highly liked alcohol advertising compared with those who did not. Conclusion: Thailand should place greater restrictions on alcohol advertising and marketing activities.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".