The impact of an integrated alcohol policy: The example of Lithuania
Bibliographic record
Abstract
INTRODUCTION: Although integrated alcohol policies, characterised by being consistent, structurally connected and interdependent, are considered to be best practices, very few evaluations of such policies exist. We evaluated the impact of two phases of integrated alcohol policies implemented in Lithuania in 2008/2009 and 2017/2018 on adult (15+ years of age) alcohol per capita consumption. METHODS: Alcohol per capita consumption was the main outcome, based on national data from Statistics Lithuania. Time-series analyses using generalised additive mixed models were used, and unrecorded consumption trends were examined. A sensitivity analysis was conducted with data from the World Health Organization. RESULTS: The two phases of integrated alcohol policies were associated with average reductions in adult alcohol per capita consumption of almost 1 litre (-0.88 L; 95% confidence interval -1.43; -0.34). Sensitivity analyses with comparable international data on Lithuania yielded similar results. DISCUSSION AND CONCLUSIONS: Integrated alcohol policies had a substantial effect on the average level of consumption. However, the effect of major single policies for Lithuania and other Baltic countries has been estimated to be of about the same magnitude. We conclude that in order to be successful, integrated alcohol policies should include at least one major effective population-based policy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".