Adaptation of and Protocol for the Validation of the Alcohol Use Disorders Identification Test (AUDIT) in the Russian Federation for Use in Primary Healthcare
Bibliographic record
Abstract
AIMS: To adapt and validate the Alcohol Use Disorders Identification Test (AUDIT) for use in the Russian Federation and countries with Russian-speaking populations by. METHODS: Systematic review of past use and validation of the Russian-language AUDIT. Interviews to be conducted with experts to identify problems encountered in the use of existing Russian-language AUDIT versions. A pilot study using a revised translation of the Russian-language AUDIT that incorporates country-specific drinking patterns in the Russian Federation. RESULTS AND CONCLUSIONS: The systematic review identified over 60 different Russian-language AUDIT versions without systematic validation studies. The main difficulties encountered with the use of the AUDIT in the Russian Federation were related to the lack of:A revised version of the Russian-language AUDIT was created based on the pilot studies, and was validated in primary healthcare facilities in all regions in 2019/2020.
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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 | 0.122 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".