How do mandated college student drinkers characterize binge drinking?
Bibliographic record
Abstract
Background Within the past 30 days, a quarter of U.S. college students reported binge drinking (i.e., consuming ≥ five drinks (male) or ≥ four drinks (female), in about 2 hours). While scholars have refined binge drinking parameters/definitions over time, determining college students’ personal characterization of binge drinking is important as misperceptions can directly impact students’ alcohol use and associated consequences.Objective This study explored differences in college students’ characterization of binge drinking and the effect of overestimating standardized parameters.Methodology This study was conducted at a large public university in the Southeast U.S. among college students who violated campus alcohol-use policies (n = 816). Respondents anonymously completed measures of alcohol frequency and quantity, and binge drinking characterization. Analyses explored the relationship between binge drinking parameters and personal alcohol use.Results Statistically significant differences existed for alcohol consumption between students who underestimated/accurately characterized binge drinking parameters and those who overestimated. Moreover, students who overestimated participated in binge drinking more frequently (p < .001) and were nearly 3.5 times more likely to binge drink than their counterparts.Conclusions Correcting misperceptions, establishing clear and accurate understandings, and eliminating ambiguity associated with alcohol behaviors and norms should be a priority for college health practitioners and administrators.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".