Utility of the Brief Young Adult Alcohol Consequences Questionnaire to Identify College Students At-Risk for Alcohol Related Problems: Relative Operating Characteristics across Seven Countries
Bibliographic record
Abstract
Background: It is important to identify students who would benefit from early interventions to reduce harmful drinking patterns and associated consequences. the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ) could be particularly useful as a screening tool in university settings. Objectives. The present study examined the utility of the B-YAACQ to distinguish among students at-risk for problematic alcohol use as measured by the AUDIT. Objectives: The present study examined the utility of the B-YAACQ to distinguish among students at-risk for problematic alcohol use as measured by the AUDIT. Methods: A sample of 6382 students (mean age=20.28, SD=3.75, 72.2% females) from seven countries (i.e., U.S., Canada, South-Africa, Spain, Argentina, Uruguay, England) completed the B-YAACQ, the AUDIT and different measures of alcohol use. Results: ROC analyses suggested that a cutoff score of 5 maximized the YAACQ’s discrimination utility to differentiate between students at low versus moderate/high risk in the total sample and across countries (except in Canada, where the cutoff was 4). In addition, a cutoff of 7 differentiated between students at low/moderate versus high risk in the total sample, while cutoffs of 10, 9, 8 and 7 differentiate between students at low/moderate versus high risk in Uruguay, U.S and Spain (10), Argentina (9), England (8), and Canada and South-Africa (7), respectively. Students classified at the three risk levels (i.e., low, moderate and high) differed in age (i.e., a younger age was associated with higher risk) and drinking patters (i.e., higher drinking frequency, quantity, binge drinking and AUDIT and B-YAACQ scores in the higher risk groups). Conclusions: This study suggest that the B-YAACQ is a useful tool to identify college students at-risk for experiencing problematic patterns of alcohol use.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".