Capturing Russian drinking patterns with the Alcohol Use Disorders Identification Test: An exploratory interview study in primary healthcare and narcology centers in Moscow
Bibliographic record
Abstract
BACKGROUND: Despite a considerable reduction in alcohol consumption, Russia has one of the highest levels of alcohol-attributable burden of disease worldwide due to heavy episodic drinking patterns. Further improvement of alcohol control measures, including early provision of screening and brief interventions (SBI), is needed. The legislative framework for delivering SBI in Russia was introduced in 2013. As part of the creation and validation of a Russian version of the Alcohol Use Disorders Identification Test (AUDIT), the present contribution explored challenges in using the AUDIT in Russia to inform a subsequent validation study of the tool. METHODS: Qualitative in-depth expert interviews with patients and healthcare professionals from four primary healthcare and narcology facilities in Moscow. A total of 25 patients were interviewed, 9 from a preventive medicine hospital, 8 from a polyclinic, and 9 from narcology clinics. Also, 12 healthcare professionals were interviewed, 5 of whom were primary healthcare doctors and 7 were narcologists. RESULTS: Patients and healthcare professionals expressed difficulties in dealing with the concept of a "standard drink" in the AUDIT, which is not used in Russia. Various patients struggled with understanding the meaning of "one drinking occasion" on the test, mainly because Russian drinking patterns center around festivities and special occasions with prolonged alcohol intake. Narcology patients had specific difficulties because many of them experienced zapoi-a dynamic drinking pattern with heavy use and a withdrawal from social life, followed by prolonged periods of abstinence. Surrogate alcohol use was described as a common marker of alcohol dependence in Russia, not accounted for in the AUDIT. CONCLUSIONS: The provided analyses on the perception of the Russian AUDIT in different patient and professional groups suggest that a series of amendments in the test should be considered to capture the specific drinking pattern and its potential harms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".