Translating and adapting the Alcohol Use Disorders Identification Test (AUDIT) for use in the Russian Federation: A multicentre pilot study to inform validation procedures
Bibliographic record
Abstract
Aims: The Alcohol Use Disorders Identification Test (AUDIT) is one of the most widely used screening instruments worldwide. Although it was translated into many languages, not many country-specific adaptations exist, and a formal validation procedure of the Russian version has been carried out only recently. The present contribution documents the different steps taken to formally translate and adapt a Russian-specific version of the AUDIT (RUS-AUDIT). Methods: The AUDIT was translated into Russian following an established protocol, revised and adapted to the country context using an expert panel, and field-tested in an iterative approach, in line with WHO rules on instrument translation and adaptation A total of three pilot phases were carried out on 134 patients from primary healthcare (PHC) and 33 patients from specialised alcohol treatment facilities (narcology), guided by a specially established advisory board. Changes in each version were informed by the findings of the previous pilot phase and a thorough panel discussion. Results: Based on the findings of three different pilot phases, the RUS-AUDIT was developed as a paper-and-pencil interview for PHC professionals. Since various issues with representation and counting of standard drinks for the second test item arose, a special show card was developed to support the assessment. Preliminary AUDIT-C scores indicated that more than one-third of the screened women (34.2%) and about half of the screened men (50.9%) from PHC facilities have exceeded risk thresholds. Conclusions: The RUS-AUDIT was constructed as a feasible assessment tool for interviewers and patients. The large number of PHC patients who exceed the risk threshold has corroborated the need for formal validation and Russia-specific cut-off scores, considering the specific drinking patterns.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".