Are Age-Based Licensing Restrictions a Meaningful Way to Enhance Rural Older Driver Safety? The Need for Exposure Considerations in Policy Development
Bibliographic record
Abstract
The stated and revealed travel behavior of a sample of 60 rural drivers aged 54-92 years provided a basis to explore the potential effectiveness of two common driver's license restrictions aimed at older drivers: time of day and road class. The potential utility and impact of these restrictions have not been explored with revealed data for jurisdictions with a large population of rural older drivers where automobile dependence is high. Data were drawn from a multiday Global Positioning System-based travel diary survey of rural older drivers in New Brunswick, Canada. Revealed travel data showed that over 50 percent of the rural drivers in the sample did not drive after dark, and 40 percent drove less than 1 percent of their total surveyed kilometers on major highways, higher rates than from participant-stated responses. The proportion of participants taking night trips and traveling on major highways decreased with age. The majority of trips taken after dark by all participants had a rural destination. The average daily kilometers driven on major highways by men and women aged 75 years and older was nearly identical (1.79 km/day). These exposure considerations suggest that restricting night travel and major highway travel for the oldest rural drivers (75 years and older) may have limited utility given that the majority of participants did not drive in these situations, and for those who did, most of their trips were in rural areas where enforcement could be expected to be limited. A better approach may be to encourage increased self-regulation through training, age-friendly upgrades to transportation infrastructure to help rural older drivers stay driving safely as long as possible, and the development of appropriate rural alternatives to help a driver transition to nondriver.
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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 | 0.071 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".