Original Research Psychiatric Distress Among Road Rage Victims and Perpetrators
Bibliographic record
Abstract
Road rage has recently appeared as a new problem for drivers in many countries, with reports coming from Australia (1), Canada (2), the UK (3), and the US (4). Newspaper reports on road rage are increasing in Canada (5) and the US (6,7). Reported cases of road rage increased by a factor of 15 in Canadian newspapers between 1996 and 2000 (5). Similarly, in the 1990s, annual newspaper reporting of road rage incidents in the US numbered in the thousands (7). There is no generally accepted definition of road rage, although it has been defined as “a situation where a driver or passenger Objective: To investigate the relation between psychiatric distress and road rage, paying particular attention to the potential link between psychiatric illness and frequent involvement in serious forms of road rage. Method: This study reports data on road rage involvement, demographic characteristics, and mental health for a representative sample of 2610 adults in Ontario. The mental health indicator was the 12-item General Health Questionnaire. Results: A cluster analysis revealed 5 distinct groups of people affected by road rage. The most serious offenders (referred to hereafter as the hard core road rage group), representing 5.5 % of those affected, exhibited frequent involvement in the most severe forms of road rage and were the most likely (27.5%) to report psychiatric distress. Conclusions: Road rage, particularly experiences of victimization, is related to psychiatric distress. Evidence of psychiatric distress was highest among hard core road rage perpetrators, individuals noted for frequent involvement in serious aggressive and violent conduct. Further research is needed on violence and road rage and its link to mental health. (Can J Psychiatry 2003;48:681–688) Information on funding and support and author affiliations appears at the end of the article.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".