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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".