Russia’s National Concept to Reduce Alcohol Abuse and Alcohol-Dependence in the Population 2010–2020: Which Policy Targets Have Been Achieved?
Bibliographic record
Abstract
In the 2000s, Russia was globally one of the top 5 countries with the highest levels of alcohol per capita consumption and prevailing risky patterns of drinking, i.e., high intake per occasion, high proportion of people drinking to intoxication, and high frequency of situations where alcohol is consumed and tolerated. In 2009, in response to these challenges, the Russian government formed the Federal Service for Alcohol Market Regulation and published a national strategy concept to reduce alcohol abuse and alcohol-dependence at the population level for the period 2010-2020. The objectives of the present contribution are to analyze the evidence base of the core components of the concept and to provide a comprehensive evaluation framework of measures implemented (process evaluation) and the achievement of the formulated targets (effect evaluation). Most of the concept's measures were found to be evidence-based and aligned with eight out of 10 areas of the World Health Organization (WHO) policy portfolio. Out of the 14 tasks, 7 were rated as achieved, and 7 as partly achieved. Ten years after the concept's adoption, alcohol consumption seems to have declined by about a third and alcohol is conceptualized as a broad risk factor for the population's health in Russia.
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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 | 0.001 |
| 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".