Collision Involvement of Older Commercial Truck Drivers
Bibliographic record
Abstract
In light of Canada’s aging population, a fair amount of attention has been given to the safety issues surrounding elderly drivers and possible relicensing or retesting strategies. It is well documented that collision rates dramatically increase as the general population of drivers progress into their senior years. Unfortunately, little is known about the relative collision involvement of aging commercial drivers. The trucking industry is struggling to manage a chronic driver shortage problem, so the employment of older drivers will likely increase. Past research has indicated driving related skills including perception and motor skills begin to decline at approximately age 65. Other studies have shown that elderly passenger vehicle drivers regulate their driving behaviour by avoiding certain high-risk driving situations. This is not always possible for older commercial vehicle drivers who may not be able to avoid driving at night, long distances, during rush hour and in construction zones. For this reason, combined with the increased responsibility associated with operating commercial vehicles and higher collision consequences, it may be prudent to impose more stringent licensing standards on aging commercial drivers. This study examines the collision involvement of older commercial truck drivers. In order to achieve this, an examination of the collision involvement of elderly commercial drivers in New Brunswick was conducted. The collision involvement analyses used the New Brunswick Department of Transportation
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".