Hospitalizations for unintentional injuries among Canadian adults in areas with a high percentage of Aboriginal-identity residents
Bibliographic record
Abstract
INTRODUCTION: Injuries are a leading cause of death and morbidity. While individual Aboriginal identifiers are not routinely available on national administrative databases, this study examines unintentional injury hospitalization, by cause, in areas with a high percentage of Aboriginal-identity residents. METHODS: Age-standardized hospitalization rates (ASHRs) and rate ratios were calculated based on 2004/2005-2009/2010 data from the Discharge Abstract Database. RESULTS: Falls were the most frequent cause of injury. For both sexes, ASHRs were highest in high-percentage First Nations-identity areas; high-percentage Métis-identity areas presented the highest overall ASHR among men aged 20-29 years, and high-percentage Inuit-identity areas presented the lowest ASHRs among men of all age groups. Some causes, such as falls, presented a high ASHR but a rate ratio similar to that for all causes combined; other causes, such as firearm injuries among men in high-percentage First Nations-identity areas, presented a relatively low ASHR but a high rate ratio. Residents of high-percentage Aboriginal-identity areas have a higher ASHR for hospitalization for injuries than residents of low-percentage Aboriginal-identity areas. CONCLUSION: Residents of high-percentage Aboriginal-identity areas also live in areas of lower socio-economic conditions, suggesting that the causes for rate differences among areas require further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".