Perception of alcohol policies by consumers of unrecorded alcohol - an exploratory qualitative interview study with patients of alcohol treatment facilities in Russia
Bibliographic record
Abstract
BACKGROUND: Over the last decade Russia has introduced various policy measures to reduce alcohol consumption and alcohol-related harm at the population level. Several of these policies, such as higher pricing and taxation or restrictions of availability, may not work in the case of unrecorded alcohol consumption; they may encourage consumers to switch to unrecorded alcohol and even increase consumption. In the present qualitative interview study we explore the perception of the recently implemented alcohol policies by patients diagnosed with alcohol dependence in two Russian cities in the years 2013-2014 and shed light on possible entry-points to prevention. METHODS: Semi-structured in-depth expert interviews were conducted with 25 patients of state-run drug and alcohol treatment centers in two Russian cities in 2013 and 2014. The interviews were analyzed using thematic content analysis. RESULTS: All of the interviewed participants have consumed unrecorded at some point with the majority being regular consumers, mostly switching between recorded and unrecorded alcohol depending on the situation, as predominantly defined by available money and available sources of alcohol. Low price and high availability were reported as the main reasons for unrecorded consumption. Participants voiced a general mistrust of the recently implemented alcohol regulations and viewed them largely as ineffective. They expressed particular concerns over price increases and restriction of night sales of alcoholic beverages. Substantial shifts within the unrecorded alcohol market were reported, with a decreasing availability of home-made beverages in favor of alcohol surrogates in the form of non-beverage alcohol, medicinal and cosmetic compounds. At the same time consumption of home-made alcoholic beverages was seen as a strategy to avoid counterfeit alcohol, which was frequently reported for retail sale. CONCLUSIONS: Despite the alcohol policy changes in the last years in Russia, consumption of unrecorded alcohol remained common for people with alcohol dependence. Reduction of availability of unrecorded alcohol, first and foremost in the form of cheap surrogates, is urgently needed to reduce alcohol-related harm. Implementation of screening and brief interventions for excessive alcohol consumption in various contexts such as primary healthcare settings, trauma treatment services or the workplace could be another important measure targeting consumers of unrecorded alcohol.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".