Calibrating the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) for detecting alcohol-related problems among Canadian, UK and US soldiers: cross-sectional pre-deployment and post-deployment survey results
Bibliographic record
Abstract
OBJECTIVES: Excessive alcohol use can bring about adverse health and work-related consequences in civilian and military populations. Screening for excessive drinking can help identify individuals at risk for alcohol-related problems who may require clinical interventions. The brief validated measures of alcohol use such as the Alcohol Use Disorders Identification Test (AUDIT), or abbreviated AUDIT-Consumption (AUDIT-C), are often included in military deployment screening and epidemiologic surveys, but appropriate cut-points must be used to effectively identify individuals at risk. Although the conventional AUDIT-C cut-points ≥4 for men and ≥3 for women are commonly used, recent validation studies of veterans and civilians recommend higher cut-points to minimise misclassification and overestimation of alcohol-related problems. This study aims to ascertain optimal AUDIT-C cut-points for detecting alcohol-related problems among serving Canadian, UK and US soldiers. DESIGN: Cross-sectional pre/post-deployment survey data were used. SETTINGS: Comprised Army locations in Canada and UK, and selected US Army units. PARTICIPANTS: Included soldiers in each of the above-mentioned settings. OUTCOME MEASURES: Soldiers' AUDIT scores for hazardous and harmful alcohol use or high levels of alcohol problems served as a benchmark against which optimal sex-specific AUDIT-C cut-points were assessed. RESULTS: Across the three-nation samples, AUDIT-C cut-points of ≥6/7 for men and ≥5/6 for women performed well in detecting hazardous and harmful alcohol use and provided comparable prevalence estimates to AUDIT scores ≥8 for men and ≥7 for women. The AUDIT-C cut-point ≥8/9 for both men and women performed fair-to-good when benchmarked against AUDIT ≥16, although inflated AUDIT-C-derived prevalence estimates and low positive predictive values were observed. CONCLUSION: This multi-national study provides valuable information regarding appropriate AUDIT-C cut-points for detecting hazardous and harmful alcohol use, and high levels of alcohol problems among soldiers. Such information can be useful for population surveillance, pre-deployment/post-deployment screening of military personnel, and clinical practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".