Can pricing deter adolescents and young adults from starting to drink: An analysis of the effect of alcohol taxation on drinking initiation among Thai adolescents and young adults
Bibliographic record
Abstract
The objective of this study is to assess the relationship between alcohol taxation changes and drinking initiation among adolescents and young adults (collectively "youth") in Thailand (a middle-income country). Using a survey panel, this study undertook an age-period-cohort analysis using four large-scale national cross-sectional surveys of alcohol consumption performed in Thailand in 2001, 2004, 2007 and 2011 (n=87,176 Thai youth, 15-24 years of age) to test the hypothesis that changes in the inflation-adjusted alcohol taxation rates are associated with drinking initiation. Regression analyses were used to examine the association between inflation-adjusted taxation increases and the prevalence of lifetime drinkers. After adjusting for potential confounders, clear cohort and age effects were observed. Furthermore, a 10% increase of the inflation-adjusted taxation rate of the total alcohol market was significantly associated with a 4.3% reduction in the prevalence of lifetime drinking among Thai youth. In conclusion, tax rate changes in Thailand from 2001 to 2011 were associated with drinking initiation among youth. Accordingly, increases in taxation may prevent drinking initiation among youth in countries with a high prevalence of abstainers and may reduce the harms caused by alcohol.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".