Injury rates in Canadian Ontario first nation communities
Bibliographic record
Abstract
Background Injuries are the leading cause of death among First Nations in Canada from 1 to 44 years, Health Canada 2001. The Ontario First Nation population was 175 178 within 133 First Nation communities in 2008. Ontario First Nations identified Motor Vehicle Collisions, Violence including Suicide and Falls, as injury issues and recommended priorities in education, training and research. An Injury Prevention Initiative was established to address issues, implement priorities and develop an Ontario First Nation Injury Prevention Strategy and Action Plan. It is coordinated by the Chiefs in Ontario. Several projects were initiated to establish baseline information upon which to develop the Strategy and Action Plan. While data on Emergency Department visits and hospitalisations are available for all Ontarians, the data does not identify First Nation people, however, residential codes provided an opportunity to determine the rate of injuries for First Nation communities. Objective To calculate the frequency of ED visits for injury in Ontario First Nations communities, stratified by sex and intent. Methods Population-based data including all ED visits in Ontario were used based on Indian Reservation residential codes assigned by the Ontario Ministry of Health and Long-Term Care. Results The results showed that although injury rates were similar to the Ontario population, the incidence of intentional injury was 2.7 times higher among First Nations, and self-inflicted injuries were 4.6 times higher. Conclusion The findings validate that injuries are a serious issue in Ontario First Nations and the strategy and action plan needs to address violence and self-inflicted injuries.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".