Alternative Avenues in the Assessment of Driving Capacities in Older Drivers and Implications for Training
Bibliographic record
Abstract
The aging of the population, combined with the overrepresentation of older drivers in car crashes, has engendered a whole body of research destined at finding simple and efficient assessment methods of driving capacities. Such a search is destined to fail, given that car crashes and unsafe driving behaviors can result from myriad interacting factors. This review highlights the main problems of the current assessment methods and training programs and presents theoretical and empirical arguments justifying the need of reorienting the research focus. In our discussion, we elaborate the fundamental principle of specificity in learning and practice. We also identify overlooked variables that are deterministic when assessing and training a complex ability like driving. We especially focus on the role of the sensorimotor transformation process. Finally, we propose alternative methods of assessment and training that, in line with recent trends in education, use virtual reality and simulation technologies.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".