The Utility of a Gender-Specific Definition of Binge Drinking on the AUDIT
Bibliographic record
Abstract
OBJECTIVE: Although binge drinking is commonly defined as the consumption of at least 5 drinks in 1 sitting for men and 4 for women, the Alcohol Use Disorders Identification Test (AUDIT) defines binge drinking as the consumption of 6 or more drinks in 1 sitting for both men and women. This study examined the effect of using gender-specific binge drinking definitions on overall AUDIT scores. PARTICIPANTS: Participants were 331 college men and 1224 college women. METHODS: Participants completed a self-report questionnaire, which included the AUDIT. RESULTS: Findings showed that defining binge drinking as 4 or more drinks for women, rather than 6 or more, does impact their AUDIT scores and could affect the percentage of women classified as hazardous users. Among men, AUDIT scores were unaffected by the use of a gender-specific definition of binge drinking. CONCLUSIONS: Results suggest that the AUDIT might be underidentifying hazardous users among college women.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".