Pregaming in high school students: Relevance to risky drinking practices, alcohol cognitions, and the social drinking context.
Bibliographic record
Abstract
Pregaming is the practice of consuming alcohol prior to going out to a social event. Although pregaming has begun to receive research attention in the college setting, very little is known about this risky drinking behavior in high school students. As pregaming has health implications for both students who are college bound and those who are not, we examined the prevalence of this behavior in a sample of high school students who reported current alcohol use and completed pregaming measures (n = 233). The present study examined the associations of gender, age, alcohol expectancies, motivations for drinking (e.g., social, enhancement, and coping), and engagement in other risky drinking practices (i.e., general hazardous use and drinking game participation) with pregaming. Results indicate that pregaming was significantly associated with being older, being a male, having high levels of hazardous alcohol use, and participating in drinking games frequently. Pregaming also occurred most often before parties and sporting events and it was associated positively with frequency of attendance at parties where alcohol is available, the tendency to use alcohol at these parties, and the amount of alcohol consumed at these parties. We discuss the findings in the context of pregaming research that has been conducted with college students, and make suggestions regarding prevention and intervention efforts focused on this risky drinking practice.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".