Ontario first nations environmental scan on injuries and injury prevention
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 inform the development of the Strategy and Action Plan. Objective The objective of the Ontario First Nations Environmental Scan on Injuries and Injury Prevention was to determine community-based injury issues, priorities, prevention initiatives and recommendations for injury prevention. Methods The method utilised was to distribute a key informant survey to each First Nation community in Ontario. Participants were asked to report the most frequent injury occurrences, to identify injury priorities, barriers and prevention initiatives including best practises. Results The results for children, identified falls and violence, for youth it was falls, violence and alcohol poisoning. Adults had motor vehicle collisions, violence, and alcohol poisoning, and elders reported falls, violence and alcohol poisoning. The lack of community involvement, education, training, research, and funding for injury prevention programs were identified broadly as recommendations. Conclusion First Nations communities understand the challenges facing their population, and want to take steps to reduce the burden of injury in their communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".