Caffeinated alcoholic beverage consumption is associated with binge drinking among Canadian college students
Bibliographic record
Abstract
Introduction: Binge drinking, characterized by a pattern of excessive alcohol intake on a single occasion, is a growing epidemic among college students. mixing alcohol with caffeinated energy drinks is also increasing in popularity. Caffeine suppresses the user’s ability to accurately assess her level of intoxication and, consequently, the user tends to drink more without realizing the effects. Few studies to date, however, have focused on the association between mixing alcohol with energy drinks and binge drinking. Methods: our study surveyed 221 Canadian college students on their mixing and binge drinking behaviours. We expected to find no significant gender differences in the proportions of both mixers and binge drinkers or in the frequencies of mixing and binge drinking. results: Binge drinkers were more likely to mix than non-binge drinkers, and mixers were more likely to binge drink than non-mixers. additionally, t-test results showed that mixers were more motivated to drink for the sake of getting drunk than non-mixers were. surprisingly, these two groups did not significantly differ in the degree to which they felt risk-related behavioral states when they consumed, even though mixers reported significantly more drinking-related life interference. conclusion: our results demonstrate that preventative programs aimed at reducing high-risk alcohol binge drinking need to consider mixing energy drinks and alcohol intake as a risk factor.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".