Improving the Driving of Community-Dwelling Older Canadians: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Older Canadians, similar to aging drivers in many other countries, want to drive, need to drive, and live in communities where driving is both valued and necessary for out-of-home participation. Many community-dwelling seniors are medically fit-to-drive, yet their collision risk remains higher than most other age groups, which some have attributed to their propensity to drive shorter distances in high-traffic areas (Antin et al., 2017). In this randomized controlled trial, the effect of a customized video-based older driver training program on behind-the-wheel performance was captured using the latest technology for an on-road evaluation. Results indicated the mean reduction in number of driving errors [mean (95% CI)=-12.0(-16.5, -7.6),p<0.001] favoured the intervention group where their change between baseline and 4-week follow-up was statistically significant [mean(95% CI)=-10.3(-13.8, -6.8),p<0.001], but not for the control group [mean (95% CI)=1.7(-0.08, 4.2), p>0.05]. Our novel, video-based approach that provided individualized feedback improved driving performance for older drivers. Part of a symposium sponsored by Transportation and Aging Interest Group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".