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