The Alcohol Use Disorders Identification Test (AUDIT) in the Russian language - a systematic review of validation efforts and application challenges
Bibliographic record
Abstract
Abstract The Alcohol Use Disorders Identification Test (AUDIT) is one of the most frequently used screening instrument for hazardous and harmful use of alcohol and potential alcohol dependence in primary health care (PHC) and other settings worldwide. It has been translated into many languages and adapted and modified for use in some countries, following formal adaptation procedures and validation studies. In the Russian Federation, the AUDIT has been used in different settings and by different health professionals, including addiction specialists (narcologists). In 2017, it was included as a screening instrument in the national guidelines of routine preventive health checks at the population-level (dispanserization). However, various Russian translations of the AUDIT are known to be in use in different settings and, so far, little is known about the empirical basis and validation of the instrument in Russia—a country, which is known for its distinct drinking patterns and their detrimental impact on health. The present contribution is the summary of two systematic reviews that were carried out to inform a planned national validation study of the AUDIT in Russia. Two systematic searches were carried out to 1) identify all validation efforts of the AUDIT in Russia and to document all reported problems encountered, and 2) identify all globally existing Russian translations of the AUDIT and document their differences and any reported issues in their application. The qualitative narrative synthesis of all studies that met the inclusion criteria of the first search highlighted the absence of any large-scale rigorous validation study of the AUDIT in primary health care in Russia, while a document analysis of all of the 122 Russian translations has revealed 61 unique versions, most of which contained inconsistencies and signaled obvious application challenges of the test. The results clearly signal the need for a validation study of the Russian AUDIT.
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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 | 0.058 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".