Executive Functions in the Evaluation of Accident Risk of Older Drivers
Bibliographic record
Abstract
The main objective of these studies was to analyse the difference in driving attitude and aptitude, between two groups of elderly male drivers (65 years or more), one being accident-free and the second having three accidents or more in the last 5 years. The first study compared the driving habits of 90 older accident-free drivers with 90 drivers having a history of accidents. The second study, on a subgroup of 60 of the original 180 subjects (30 accident-free and 30 having accidents), compared cognitive function, with particular emphasis on executive functions as measured by neuropsychological tests, and attitude and self-reported driving behaviour. The results show that elderly drivers having a history of accidents, compared to the control group: (1) have poorer performance on the four cognitive measurements of executive functions; (2) report to have more prudent behaviour on the road (e.g., reducing their speed); and (3) have the intention to adopt less risky driving behaviour. This study suggests that a subgroup of the older driver population has cognitive problems and driving disabilities that cannot be compensated by apparently more careful behaviour on the road. The results confirm the importance of proper assessment of cognitive processes and underscore the potential of measuring executive functions for the evaluation of driving competence of elderly persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".