Restricted driver licensing for medical impairments: does it work?
Bibliographic record
Abstract
BACKGROUND: Medical conditions may adversely affect driving ability. Many North American jurisdictions provide restricted driving licences that permit people with certain medical conditions to drive under limited conditions, but the effectiveness of such programs has not yet been determined. The objectives of this study were to evaluate the rates of crashes and traffic violations among drivers with a restricted licence, compared with the rates in the general driving population, and to compare the crash and traffic violation rates before and after driving restrictions were imposed. METHODS: We retrospectively analyzed a cohort of all licensed Saskatchewan drivers registered from Jan. 1, 1992, to Apr. 19, 1999. The cohort was divided into those with a restricted licence and those with an unrestricted general licence. We used multivariate Poisson regression to calculate incidence rate ratios (IRRs) for at-fault crashes and traffic violations, adjusting for age, sex and residence (urban v. rural). We used interventional time series analysis to compare rates of crashes and traffic violations before and after the imposition of driving restrictions. RESULTS: Of the 703,758 drivers in the study, 23,185 (3.3%) had a restricted licence. Restricted licence holders had a higher crash rate than drivers without restrictions (adjusted IRR 1.13, 95% confidence interval [CI] 1.11-1.17). However, this rate was lower than that among male drivers (adjusted IRR 2.01, 95% CI 1.99-2.02) and urban drivers (adjusted IRR 1.38, 95% CI 1.37-1.39). Drivers with restricted licences had a lower traffic violation rate than those without restrictions (adjusted IRR 0.93, 95% CI 0.91-0.95). At-fault crash rates decreased by 12.8% (95% CI 2.4%-23.2%) and adjusted traffic violation rates decreased by 10.0% (95% CI 4.4%-15.7%) after restrictions were imposed. During the study period, licence restrictions likely averted up to 816 crashes and 751 traffic violations. INTERPRETATION: Province-wide population data suggest that a restricted licensing program appears to provide a significant decrease in the rates of crashes and traffic violations.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".