Alcoholism, Tobacco, and Drug Use in the Countries of Central and Eastern Europe and the Former Soviet Union
Bibliographic record
Abstract
This note reviews recent literature relating to the use of alcohol, tobacco, and intravenous drugs in the countries of Central and Eastern Europe and the former Soviet Union. Trends in substance use among subpopulations are discussed in relation to changes in the major population health indicators since 1990. This article summarizes recent work into understanding the role of substance use and misuse in the region in explaining demographic developments in terms of life expectancy and population growth. In 1993, 80% of Russian males drank, and among these, alcohol intake averaged near 600 grams per day. Smoking is much more prevalent among Russian men (61.4%) than among women (10.3%). In 2003, 185.8 per 100 thousand Russian men were drug addicted. High rates of male alcohol abuse, suicide, accidents, violence, and cardiovascular disease appear to be major causes of the large falls in life expectancy and rising gender gaps in life expectancy in the region. Life expectancy at birth in Russia was estimated at 60.5 years for males and 74 years for females in 2005. Russia's population is now declining at a rate of 0.37% per annum, and with more than 1% of the population estimated to be HIV positive, it is likely that this population decline will accelerate further in the near future. Many of these population trends are mirrored across the former Soviet Union.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".