Predicting Older Drivers' Difficulties Using the Roadwise Review
Bibliographic record
Abstract
There has been a substantial growth in research attempting to predict accidents and performance in older drivers. The Roadwise Review and the substantively identical Driver Health Inventory have been reported to provide a valid and cost-effective means of assessing crash risk in older communitydwelling adults. We administered the DHI to a community-dwelling sample of older (45 - 85 years) drivers. We also asked them to report on the difficulties they experienced while driving and on the frequency and type of crashes and moving violations the experienced in the previous two years. Results indicated on several of the tests there are substantial floor or ceiling effects, as well as barriers to usability and acceptance. Low inter-test correlations are consistent with the notion that different capacities are being indexed with the DHI. However, generally there were only low correlations between DHI performance and self-reported difficulties in driving, accidents or moving violations. While the DHI and Roadwise Review may well be valuable in providing older drivers with information on skills related to driving performance, in its current form it does not appear to be a useful tool in licensure or the prediction of driver risk.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".