Alcohol consumption and problems among road rage victims and perpetrators.
Bibliographic record
Abstract
OBJECTIVE: Road rage has generated public concern; however, data on the causes of this behavior have not been available. We examine the alcohol consumption correlates of road rage victimization and perpetration based on a population survey of adults. METHOD: Data are based on the 2001-2002 Centre for Addiction and Mental Health Monitor, a repeated cross-sectional telephone survey of Ontario adults aged 18 and older (N = 2,610). Logistic regression analyses were performed with drinking measures (Alcohol Use Disorders Identification Test [AUDIT] consumption, dependence and problems) and demographic factors as independent variables. RESULTS: In the past year, 44.4% of respondents reported that someone shouted, cursed or made rude gestures at them, 6.0% were threatened with damage to their vehicle or personal injury, and 5.2% had someone attempt to or actually damage their vehicle or hurt them. Over the same period, 32% admitted shouting, etc., at someone, 1.7% threatened someone, and 1.0% attempted to or actually did damage someone's vehicle or hurt someone. Univariate analyses revealed several significant relationships between road rage and alcohol measures. Multivariate analyses revealed that the AUDIT alcohol problems measure was most consistently associated with measures of road rage victimization and perpetration, including reporting attempting or actually hurting someone or attempting or actually damaging his or her vehicle. CONCLUSIONS: These data indicate there is a significant relationship between alcohol problems, as measured by the AUDIT, and road victimization and perpetration. Further work must be undertaken to identify the mechanisms involved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".