Reduction in Fatalities, Ambulance Calls, and Hospital Admissions for Road Trauma After Implementation of New Traffic Laws
Bibliographic record
Abstract
OBJECTIVES: We evaluated the public health benefits of traffic laws targeting speeding and drunk drivers (British Columbia, Canada, September 2010). METHODS: We studied fatal crashes and ambulance dispatches and hospital admissions for road trauma, using interrupted time series with multiple nonequivalent comparison series. We determined estimates of effect using linear regression models incorporating an autoregressive integrated moving average error term. We used neighboring jurisdictions (Alberta, Saskatchewan, Washington State) as external controls. RESULTS: In the 2 years after implementation of the new laws, significant decreases occurred in fatal crashes (21.0%; 95% confidence interval [CI]=15.3, 26.4) and in hospital admissions (8.0%; 95% CI=0.6, 14.9) and ambulance calls (7.2%; 95% CI=1.1, 13.0) for road trauma. We found a very large reduction in alcohol-related fatal crashes (52.0%; 95% CI=34.5, 69.5), and the benefits of the new laws are likely primarily the result of a reduction in drinking and driving. CONCLUSIONS: These findings suggest that laws calling for immediate sanctions for dangerous drivers can reduce road trauma and should be supported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".