Global alcohol policy implementation in Thailand: A narrative review
Bibliographic record
Abstract
Alcohol consumption is growing in emerging economies. Thailand is an upper-middle-income emerging economy in Southeast Asia where the alcohol market has been under an oligopoly, which political groups are attempting to amend. The Thai Ministry of Public Health works in close partnership with the non-profit sector in alcohol control, and has recently adapted the World Health Organization’s (WHO) SAFER initiatives as part of the national alcohol agenda. In light of these ongoing changes, we conducted a narrative review with the following objectives: (1) to describe the development of alcohol control policy strategies in Thailand; and (2) to describe the current situation on alcohol policy implementation. We performed a search of the published and grey literature in both Thai and English languages using keywords related to topics of interest, and reviewed data on SAFER's effectiveness using a group of indicators (policy-attributable health outcomes). We found that Thailand has laws and regulations covering various domains of SAFER. However, gaps do exist with regard to regulation of alcohol outlet density, relatively low alcohol sales permit fees, control of online and surrogate marketing of alcoholic products, and enforcement of drinking-and-driving deterrence measures, among others. A review of the indicators showed that annual per capita alcohol consumption (APC) and prevalence of drinkers have remained constant since 2008, whereas the prevalence of binge-drinking has been on a decline since 2014. However, deaths attributable to alcohol have increased in both men and women. The findings of this narrative review may have implications for policymakers and stakeholders in behavioral health.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".