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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".