Evaluation of the national alcohol control strategy (Green Paper on Alcohol Policy) of Estonia
Bibliographic record
Abstract
INTRODUCTION: Estonia is a Baltic country with high adult alcohol per capita (APC) consumption. Since 2013, its alcohol control policy has been guided by the Green Paper on Alcohol Policy (GP), which is the equivalent of a non-binding national alcohol action plan. This contribution attempts to evaluate the overall impact of the GP on APC. METHODS: For the overall evaluation, APC was quantitatively compared for three periods: pre-GP (2000-2012), the core period of the GP (2013-2019) and the COVID-19 phase (2020-2022), using Analysis of Variance. RESULTS: APC decreased on average by 0.25 L of pure alcohol per year in the 7 years defined as the core period of the GP, whereas it increased in the other periods between 2001 and 2022 (period 2001-2012: +0.47 L; 2020-2022: +0.27 L). These differences were statistically significant (F [1, 18] = 5.22, p = 0.035). Moreover, there was no overall trend of decreasing APC during the core period of the GP in neighbouring countries (Latvia, Lithuania and Poland). DISCUSSION AND CONCLUSIONS: The combination of the various measures of the national alcohol policy was associated with a marked decrease in APC.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".