The role of personality in predicting unsafe driver behaviour in young and older adults
Bibliographic record
Abstract
Traffic-related collisions represent a considerable social and economic burden on our society. In Canada, drivers aged 16 to 19 years and 70 years or older are consistently overrepresented in victim statistics. Identified risk factors for crash involvement are quite different among young and older drivers. One factor consistently linked to dangerous driving among young and middle-aged adults is personality. Surprisingly, little research has examined the role of personality characteristics in dangerous driving behaviour among older adults. Given the empirical evidence demonstrating a relationship between personality and driver safety among younger populations and the relative stability of personality throughout adulthood, the question arises as to whether personality features recognized to be related to dangerous driving in young adults are also related to dangerous driving in older adults. One hundred and fourteen active drivers ranging in age from 18 to 89 years (M = 42.30 years) were recruited. In addition to capturing self-reported information regarding driving performance and personality, cognitive and simulated driving performance data was collected on all participants. Overall, the current study suggests that personality appears to play some role in the prediction of observational driving performance among older adults. While unexpected, personality did not emerge as a significant predictor of self-reported unsafe driver behaviour. Furthermore, our results suggest that personality may not have equal effects on all groups of drivers. As this study is the first observational investigation of the role of personality in the prediction of unsafe driving behaviour among both young and older adults concurrently, further investigation is needed to provide more conclusive inferences. Future research directions are discussed.
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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 | 0.001 |
| 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".