Drinking patterns and harm of unrecorded alcohol in Russia: a qualitative interview study
Bibliographic record
Abstract
<b>Background:</b> Consumption of unrecorded alcohol (alcohol, consumed as a beverage, but not reflected in official statistics) has been linked to heavy drinking and alcohol-related mortality in Russia, with different studies looking for possible toxic components or other explanations. This study explores self-reported drinking behaviors of people diagnosed with alcohol dependence to elicit the perspectives of consumers of unrecorded alcohol. <b>Methods:</b> Semi-structured in-depth expert interviews were conducted with patients (<i>n</i> = 25) of state-run addiction treatment centers of two Russian cities. Interviews were analyzed using thematic content analysis. <b>Results:</b> A strict hierarchy between different types of unrecorded alcohol products, their ascribed quality, and the subjective harm caused by their consumption was found, with home-made spirits for own consumption at the top and technical fluids at the bottom. The ranking order correlated with product price, social status of associated consumers, and severity of their alcohol dependence. Binge drinking was the prevailing drinking pattern and shifts from recorded to unrecorded consumption within a single binge or a zapoi (continuous drinking for at least two days) were typical. Consumption of low-quality unrecorded alcohol was associated with stronger hang-overs, zapois, alcohol psychoses and poisonings, and other indicators of alcohol attributable harm, while no such connection was found for spirits for own consumption. <b>Conclusions:</b> In the dominant explanation patterns of the consumers, the experienced alcohol-induced harm is attributed to alcohol quality, while a thorough analysis of their reported drinking behaviors cannot exclude specific drinking patterns linked to the severity of alcohol dependence as the main determinants of the described health detriments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".