Aggression involving alcohol: relationship to drinking patterns and social context
Bibliographic record
Abstract
AIMS: The present study examines the relationships between: (1) alcohol involvement/perceived intoxication level of participants and aggression severity; (2) respondent drinking patterns and involvement in alcohol-related aggression; and (3) social context and alcohol-related aggression. DESIGN: Random digit dialing (RDD) with computer assisted telephone interviewing (CATI) was used to obtain a random sample of Ontario adults aged 18-60 (response rate of 67%). PARTICIPANTS: Respondents who reported that they had been involved personally in physical aggression in the past 12 months were the focus of the present study. MEASUREMENTS: Questions were asked regarding the most recent incident of physical aggression, including whether the respondent and opponent drank alcohol prior to aggression, perceived intoxication levels at the time, number of participants, relationship to opponent, social context of aggression, time of day and day of week. Three items were used to assess aggression severity: injury to respondent, use of threats by respondent or opponent and police involvement. FINDINGS: (1) Injury to respondent and threats by respondent were not associated with alcohol involvement per se, but were significantly related to perceived level of alcohol intoxication; (2) drinking pattern of respondent was significantly associated with alcohol-related aggression but unrelated to aggression that did not involve alcohol; and (3) a number of contextual factors (e.g. gender, number of participants, time of day) were found to be associated with alcohol involvement in aggression. CONCLUSIONS: The results suggest that both drinking pattern and contextual factors are important in distinguishing between alcohol-related aggression and non-alcohol-related aggression. As well, alcohol intoxication may be an important predictor of aggression severity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".