Type A Behavior Pattern, Risky Driving Behaviors, and Serious Road Traffic Accidents: A Prospective Study of the GAZEL Cohort
Bibliographic record
Abstract
The type A behavior pattern (TABP), characterized by impatience, time urgency, and hostility, was originally developed in relation to coronary heart disease. Since 1986, there has been a debate on whether the TABP is also associated with risky driving behaviors leading to increased risks in road traffic accidents (RTAs). The authors examined prospectively the relation among risky driving behaviors, serious RTAs, and the TABP in a cohort of 20,000 French employees of Electricité de France-Gaz de France who were aged 39-54 years in 1993. A total of 11,965 participants were included in this study. The TABP was assessed in 1993 using the French version of the Bortner Rating Scale. Driving behaviors and serious RTAs were recorded in 2001. Sociodemographic and alcohol consumption data were available from the cohort's annual follow-up. The impact of the TABP on the risk of serious RTAs was assessed using the Cox proportional hazards regression model with time-dependent covariates. After adjustment for potential confounders, the risk for serious RTAs increased proportionally with TABP scores: hazard ratios were 1.29 (95% confidence interval: 1.03, 1.63) for intermediate-level scores and 1.48 (95% confidence interval: 1.16, 1.90) for high-level scores relative to low TABP scores. The authors concluded that type A drivers had an increased risk of RTAs. Implications of this finding for traffic safety are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".