Factors influencing complete abstinence during Thailand’s temporary alcohol abstinence campaign
Bibliographic record
Abstract
Thailand's temporary abstinence campaign aims to persuade drinkers to abstain from alcohol for three months during Buddhist Lent. In recent years, a decline in the popularity of the campaign has been observed. This study aims to determine factors associated with success in complete abstinence during the campaign period and to determine whether the associations change over time to provide insight into the decline in complete abstinence. This study analyzes pooled data of 5898 current drinkers from three waves (2015, 2018, and 2021) of the campaign evaluation survey. The primary outcome is complete abstinence during the campaign. Multivariable analysis indicated that campaign media exposure was associated with complete abstinence (OR, 1.42; 95% CI 1.17-1.72). Similarly, the year 2018, older age, lesser drinking frequency prior to the campaign, and higher level of affordability were positively associated with complete abstinence. There was a statistically significant interaction between year and drinking frequency prior to the campaign (p < 0.001). The decline in complete abstinence was plausibly explained by reduced campaign media exposure, increased drinking frequency among drinkers, and the 2021 period effect (presumably COVID-19). Diversifying campaign media distribution across traditional, community-based, and digital platforms may enhance the campaign's success by ensuring wider exposure to campaign messages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".