Are we homogenising risk factors for public health surveillance? Variability in severe injuries on First Nations reserves in British Columbia, 2001–5
Bibliographic record
Abstract
BACKGROUND: Aboriginal Canadians are considered to be at increased risk of injury. The de facto standard for measuring injury risk factors among Aboriginal Canadians is to compare hospitalisation and mortality against non-Aboriginal Canadians, but this may be too broad an approach for injury prevention and public health if it over-generalises injury risk. METHODS: Data from this study are drawn from the 2001-5 British Columbia Trauma Registry and British Columbia Coroner's Service. Observed and expected hospitalisations and mortality rates on reserves were assessed against three different spatial aggregations of non-reserve reference populations. Data analysis was conducted in a geographical information system using a Poisson probability map. RESULTS: A total of 47 (9.6%) of 487 reserves in British Columbia contained at least one person who was hospitalised or died as a result of serious injury during the study period. Of these, two reserve populations represented 20% (n=19) of all injury morbidity events and 30% (n=22) of all mortality events. CONCLUSION: Evidence from this study suggests that community-based rather than provincial-based injury reporting is less likely to over-generalise the burden of injury among Aboriginal communities. Community-based surveillance enables researchers to identify why severe unintentional and intentional injury continues to burden some communities but not others and avoids the potentially demoralising and stigmatising effects of current surveillance practices.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".