Field Investigation of College Student Alcohol Intoxication and Return Transportation from At-Risk Drinking Locations
Bibliographic record
Abstract
Each year hundreds of youths’ lives are lost as a result of alcohol-impaired driving. College students leaving at-risk drinking environments are at particular risk for harm. Yet, little field research has been performed to examine college student transportation choices paired with breath alcohol testing of intoxication. This study assessed the transportation decisions of 7,500 individuals as they left drinking establishments near a large, public university in the southeastern United States. Across 3 years and 72 nights, researchers outside local drinking establishments recruited passersby who agreed to provide their planned transportation method for returning home and their blood alcohol concentration (BAC) from a police-quality breathalyzer. The results indicated that the majority of students were reaching high levels of intoxication. Indeed, the average BAC of drinking participants was .0979 g/dL. More than 50% of the participants reported that they were planning to walk home. Approximately one-quarter of the participants planned to use a designated driver. Fewer than 5% of the participants were unsure about how they were going to get home. Significant differences in BAC were observed as a function of the anticipated method for returning home. Contrary to several previous studies, individuals with designated drivers did not have higher BACs than most other individuals. The BACs of self-reported drivers were of particular concern. Although 36.7% of drivers were completely sober, the average BAC of drinking drivers was .061 g/dL. Furthermore, 39.8% of drivers with BACs over .08 g/dL believed that they were under the legal limit to drive. The results suggest that intervention efforts should focus on promoting safe and completely sober designated drivers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".