The RISC research project: injury in First Nations communities in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: The project, Injury in British Columbia's Aboriginal Communities: Building Capacity while Developing Knowledge, funded by the Canadian Institutes of Health Research (CIHR), aims to expand knowledge on injury rates among First Nations communities in British Columbia (BC), Canada. OBJECTIVE: The purpose is to improve understanding of community differences and to identify community-level risk and protective factors. Generally, injury incidence rates in the Aboriginal population in Canada greatly exceed those found in the non-Aboriginal population; however, variability exists between Aboriginal communities, which have important implications for prevention. DESIGN: This study uses administrative records of deaths, hospitalizations, ambulatory care episodes, and workers' compensation claims due to injuries to identify communities that have been especially successful in maintaining low rates of injury. RESULTS: The analysis of risk and protective factors extends the work of Chandler and Lalonde who observed that community efforts to preserve and promote Aboriginal culture and to maintain local control over community life are strongly associated with lower suicide rates. CONCLUSION: The discussion on psychological and cultural considerations on healing and reducing the rates of injury expands the work of McCormick on substance use in Aboriginal 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".