Comparison of On-Reserve Road Versus Off-Reserve Road Motor Vehicle Crashes in Saskatchewan, Canada
Bibliographic record
Abstract
BACKGROUND: There is an overwhelmingly high incidence of severe injuries caused by motor vehicle crashes (MVCs) among Aboriginal Canadians as compared with the general population. METHODS: The authors obtained MVC data for a 3-year period, 2003-2005, from Saskatchewan Government Insurance (SGI) for collisions occurring on on-reserve roads (n = 1270) together with a randomly selected sample of MVCs from off-reserve roads (n = 1270) in Saskatchewan. They compared the collision characteristics using bivariate and multiple logistic regressions. RESULTS: On-reserve MVCs were more likely to include multiple collisions and result in severe injuries than the off-reserve sample. A number of factors were significantly related to the increased risk of on-reserve collisions as compared with the reference group for each variable. INTERPRETATION: Factors from all 3 levels (human, environmental, and vehicle factors) are associated with on-reserve MVCs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".