Estimating the Burden of Injuries Among the Métis Nation of Alberta, Canada
Bibliographic record
Abstract
Background The Métis represent one of three recognized Aboriginal groups within Canada. The term Métis is used to describe people with mixed First Nations and European heritage, who have their own distinct culture and traditions. Little information exists on the burden of injuries in this population. The present study examined injury-related health services use (hospital admissions and emergency department visits) and mortality among members of the Métis Nation of Alberta comparing results with the whole Alberta population. Methods This population-based descriptive study used administrative data maintained by the Alberta Ministry of Health (AH), for the year 2013. Hospital inpatient and emergency department data as well as Alberta Vital Statistics mortality data were linked using a unique personal health number. To identify injury and mortality cases among the Métis Nation of Alberta people, administrative databases were deterministically linked to the Métis Nation of Alberta Identification Registry. Age-standardized rates of injury-related health services usage were analyzed. Results Age-standardized incidence rates (ASIR) for all causes of injury combined were significantly higher with emergency department (ED) and hospital admissions being 35% (p < 0.01) and 26% (p = 0.05) higher, respectively than the non-Métis population. ASIRs for health services use were also higher among the Métis living in rural areas (p < 0.01) and among men (p < 0.01). Injury-related mortality did not differ between the Métis and non-Métis populations. However, among the Métis, Males had significantly higher injury mortality rates than females (p < 0.05). Conclusions Results from the present study suggest that injuries are important aspects to be addressed with Métis people. Health planners should design and implement strategies directed to reduce the burden of injury and associated complications for Métis people, especially in the rural area and among Métis males. Key messages Injuries are a significant health burden within the Métis population, particularly when compared to the general Alberta population Rural living was associated with a higher injury rate among the Métis population
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.001 | 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".