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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".