Alcohol control policies in Former Soviet Union countries: A narrative review of three decades of policy changes and their apparent effects
Bibliographic record
Abstract
ISSUES: The last Soviet anti-alcohol campaign of 1985 resulted in considerably reduced alcohol consumption and saved thousands of lives. But once the campaign's policies were abandoned and the Soviet alcohol monopoly broken up, a steep rise in mortality was observed in many of the newly formed successor countries, although some kept their monopolies. Almost 30 years after the campaign's end, the region faces diverse challenges in relation to alcohol. APPROACH: The present narrative review sheds light on recent drinking trends and alcohol policy developments in the 15 Former Soviet Union (FSU) countries, highlighting the most important setbacks, achievements and best practices. Vignettes of alcohol control policies in Belarus, Estonia, Kazakhstan, Lithuania and Uzbekistan are presented to illustrate the recent developments. KEY FINDINGS: Over the past decade, drinking levels have declined in almost all FSU countries, paralleled by the introduction of various alcohol-control measures. The so-called three 'best buys' put forward by the World Health Organization to reduce alcohol-attributable burden (taxation and other measures to increase price, restrictions on alcohol availability and marketing) are relatively well implemented across the countries. IMPLICATIONS: In recent years, evidence-based alcohol policies have been actively implemented as a response to the enormous alcohol-attributable burden in many of the countries, although there is big variance across and within different jurisdictions. CONCLUSION: Strong declines in alcohol consumption were observed in the 15 FSU countries, which have introduced various alcohol control measures in recent years, resulting in a reduction of alcohol consumption in the World Health Organization European region overall.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".