A Community Initiative to Increase Use of Seat Belts in Northern British Columbia: Impacts on Casualty Crashes
Bibliographic record
Abstract
OBJECTIVE: The study aimed to establish an association between seat belt ticketing by police, seat belt wearing rates, and decreases in casualty rates following the implementation of a community seat belt initiative in a northern region of British Columbia. METHODS: Annual and monthly violation ticket rates and the percentage of casualties unbelted in collisions were computed for the North Central region and a comparison region, the Southern Interior. The trends in annual seat belt ticket rates, seat belt use among injured victims, and injury data from 2001 through 2007 were examined by descriptive/univariate methods and with intervention time series analysis. Use of a casualty rate measure controlled for changes in collision frequency over time. The primary outcome measure was injury claim incidents involving injuries other than to soft tissue. Injury claims involving only soft tissue were examined as a control series, because it was reasoned this subset of casualties would be less impacted by seat belt use. RESULTS: Seat belt tickets per capita increased in the North Central (NC) region over the study period, exceeding levels in all other regions. The percentage of unbelted occupants in casualty crashes fell by about 3 percent per year in the NC region after the initiative was introduced compared to about 1 percent per year in the SI region. The time series models revealed a significant reduction in non soft tissue casualties per 100 collisions per month. No significant reduction in the soft tissue only injury criterion was detected. CONCLUSIONS: A strong community initiative backed by support at the provincial level can be successful in a largely rural and sparsely populated northern region despite the challenges faced in such regions.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".