Utility of the Brief Young Adult Alcohol Consequences Questionnaire-Drinking Game (B-YAACQ-DG) scale in screening hazardous alcohol use among university student drinking gamers in the United States
Bibliographic record
Abstract
Background Many young adults who are enrolled in a university play drinking games (DG), a risky drinking practice that is known to facilitate heavy alcohol consumption. DG participation is associated with increased risk for harm and as such, identifying university students at risk for experiencing DG harms is key. Standardized instruments designed to assess DG behavior and related outcomes are limited by having no established cutoffs to identify those individuals who play DG who are at risk for hazardous alcohol use. In the present study, we developed a short form of the Brief Young Adult Alcohol Consequences Questionnaire-Drinking Games (B-YAACQ-DG) for use among university student samples based on differential item functioning (DIF)-free items across gender, and established cutoffs indicating hazardous use using receiver-operating characteristic curves with AUDIT scores of 6+ as the reference standard.Method Students (N = 1,299; ages 18–25; 67.7% women; 71.0% White) from four large public universities in the United States completed a confidential online survey.Results Although gender-based DIF on several items on the B-YAACQ-DG emerged, there were 16-items that were DIF-free across men and women. An optimal cutoff score for detection of hazardous use for the full version (23-items) of the B-YAACQ-DG was experiencing 4+ DG consequences in the past month, and 3+ consequences for the 16-item DIF-free version.Conclusions Health practitioners and alcohol researchers can use the B-YAACQ-DG alongside the AUDIT, to identify university students who play DGs that are in need of intervention. The B-YAACQ-DG can also be used to assess specific DG harms experienced among students who play DGs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".