Intentional injury hospitalizations in geographical areas with a high percentage of Aboriginal-identity residents, 2004/2005 to 2009/2010
Bibliographic record
Abstract
INTRODUCTION: This study describes rates of self-inflicted and assault-related injury hospitalizations in areas with a relatively high percentage of residents identifying as First Nations, Métis and Inuit, by injury cause, age group and sex. METHODS: All separation records from acute in-patient hospitals for Canadian provinces and territories excluding Quebec were obtained from the Discharge Abstract Database. Dissemination areas with more than 33% of residents reporting an Aboriginal identity in the 2006 Census were categorized as high-percentage Aboriginal-identity areas. RESULTS: Overall, in high-percentage Aboriginal-identity areas, age-standardized hospitalization rates (ASHRs) for self-inflicted injuries were higher among females, while ASHRs for assault-related injuries were higher among males. Residents of high-percentage Aboriginal-identity areas were at least three times more likely to be hospitalized due to a self-inflicted injury and at least five times more likely to be hospitalized due to an assault-related injury compared with those living in low-percentage Aboriginal-identity areas. CONCLUSION: Future research should examine co-morbidities, socio-economic conditions and individual risk behaviours as factors associated with intentional injury hospitalizations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".