Comparing rural traffic safety in Canada and Australia: a scoping review of the literature
Bibliographic record
Abstract
INTRODUCTION: The reduction of road fatalities is a priority established by the WHO and ratified by the UN. Rates of road fatalities are disproportionately high in rural areas in both Australia and Canada, two Commonwealth countries with comparable healthcare systems and rural health challenges. The purpose of this review was to compare and contrast the epidemiology, risk factors and prevention strategies of rural road fatalities in both countries to inform the next steps for prevention. METHODS: A scoping literature review was undertaken systematically to search for peer-reviewed literature published from January 2000 to June 2021. Articles were reviewed from five databases (EMCARE, Medline, CINAHL, Scopus and Informit). Search terms were adapted to suit each database and included combinations of keywords such as 'traffic accident', 'fatality', 'rural/remote', 'Australia' and 'Canada'. Themes and data associated with the research outcomes were extracted and tabulated. RESULTS: Forty-three papers were identified as relevant: 14 exploring epidemiology, 25 investigating risk factors and 37 proposing prevention strategies. People living in rural locations were 3.2 (95% confidence interval: 3.0-3.5) times more likely than urban dwellers to die in road-related incidents, with rates of motor vehicle fatalities universally higher. Common risk factors included drugs and alcohol, speed, driver error and biological sex. Key prevention strategies included improved infrastructure, vehicle design, impaired driving prevention and education. CONCLUSION: Further research regarding preventative measures and significant investment in rural road safety in both Australia and Canada are needed to prevent future incidents.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.037 | 0.057 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".