Where, With Whom, and How Much Alcohol Is Consumed on Drinking Events Involving Aggression? Event‐Level Associations in a Canadian National Survey of University Students
Bibliographic record
Abstract
BACKGROUND: Epidemiological research using event-level data can provide a better understanding of the association between alcohol consumption, characteristics of drinking contexts, and the likelihood of aggressive behavior. The present research assessed whether alcohol intake and characteristics of the drinking context were associated with the likelihood of aggression within individuals across 3 drinking events based on a national sample of university students, taking into account individual characteristics and university level variables. Additionally, we determined whether individual characteristics, particularly drinking pattern, were associated with alcohol-related aggression controlling for drinking event characteristics, and whether relations of aggression to alcohol and drinking contexts differed by gender. METHODS: Secondary analyses of the 2004 Canadian Campus Survey (CCS), a national survey of 6,282 university students (41% response rate) at 40 Canadian universities, were conducted. Respondents were asked about their three most recent drinking events, including whether they were in an argument or fight with someone, number of drinks consumed, and characteristics of the drinking context as well as their usual drinking frequency and heavy episodic drinking. We used multi-level analyses to account for the nested structure of the data (i.e., drinking events nested within individuals who were nested within universities). RESULTS: The number of drinks consumed was positively associated with aggression. Drinking contexts found to be positively associated with aggression included being at a party, at a fraternity/sorority and/or residence, at three or more drinking places (versus 1 or 2), and having a partner present whereas having a meal reduced the likelihood of aggression. A significant interaction was found between gender and being at a party, with a significant effect found for women but not for men. CONCLUSIONS: These results support experimental evidence indicating a direct role of alcohol in aggression and point to characteristics of the drinking context that might be targeted in future prevention initiatives.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".