Alcohol use disorders among Myanmar migrant workers in Thailand
Bibliographic record
Abstract
Purpose The purpose of this paper is to determine the prevalence of Alcohol Use Disorders (AUDs) among Myanmar male migrant workers (> 15 years) living in Mae Sot, Thailand, and their patterns of drinking. Design/methodology/approach A cross-sectional survey was administered to 512 participants to measure AUDs and drinking patterns. ANOVA and χ2 analyses were performed to assess demographic differences between abstainers, harmful and hazardous drinkers (HHDs) (those showing signs of AUDs) and non-harmful drinkers. Findings Results showed that 12.3 percent of male Myanmar migrants were HHDs, a rate only slightly higher than in Thai men (9.1 percent), but much higher than in men still living in Myanmar (2.7 percent) (WHO, 2014). Also, 19 percent of alcohol-consuming Myanmar male migrant workers reported patterns of heavy episodic drinking, which is markedly higher than in alcohol-consuming Thai (4.7 percent) and Myanmar men (1.5 percent) (WHO, 2014). Originality/value Given the health risks associated with AUDs and heavy episodic drinking, the findings of this study suggest a need for appropriate alcohol-related health education and intervention for Myanmar male migrant workers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".