The rural–urban gap: differences in injury characteristics
Bibliographic record
Abstract
BACKGROUND: Injuries are among the top 10 leading causes of death in Canada. However, the types and rates of injuries vary between rural versus urban settings. Injury rates increase with rurality, particularly those related to motor vehicle collisions. Factors such as type of work, hazardous environments and longer driving distances contribute to the difference in rural and urban injury rates. Further examination of injuries comparing rural and urban settings with increased granularity in the nature of injuries and severity is needed. METHODS: The study population consisted of records from the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) from between 2011 and July 2017. Rural and urban status was determined based on postal codes as defined by Canada Post. Proportionate injury ratios (PIRs) were calculated to compare rural and urban injury rates by nature and severity of injury and sex, among other factors. RESULTS: Rural injuries were more likely to involve multiple injuries (PIR = 1.66 for 3 injuries) and crush injuries (PIR = 1.72). More modestly elevated PIRs for rural settings were found for animal bites (1.14), burns (1.22), eye injuries (1.32), fractures (1.20) and muscle or soft tissue injuries (1.11). Injuries in rural areas were more severe, with a higher likelihood of cases being admitted to hospital (1.97), and they were more likely to be due to a motor vehicle collision (2.12). CONCLUSION: The nature of injuries in rural settings differ from those in urban settings. This suggests a need to evaluate current injury prevention efforts in rural settings with the aim to close the gap between rural and urban injury rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".