Unintentional injury hospitalizations and socio-economic status in areas with a high percentage of First Nations identity residents.
Bibliographic record
Abstract
BACKGROUND: Few national studies of hospitalizations due to injuries among the First Nations population have been conducted. DATA AND METHODS: Based on 2004/2005 to 2009/2010 data from the Discharge Abstract Database, this study examines associations between unintentional injury hospitalizations, socio-economic status and location relative to an urban core in Dissemination Areas (DAs) with a high percentage of First Nations identity residents versus a low percentage of Aboriginal identity residents. RESULTS: Unintentional injury hospitalization rates were higher in the less affluent and the most remote DAs. When DAs with the same socio-economic status and location were compared, the risk of hospitalizations was greater in high-percentage First Nations identity DAs relative to low-percentage Aboriginal identity DAs. INTERPRETATION: Socio-economic conditions and remote location accounted for some, but not all, of the differences in unintentional injury hospitalizations between high-percentage First Nations identity and low-percentage Aboriginal identity DAs. This suggests that characteristics not measured in this analysis--such as environmental, behavioural or other factors--play an additional role in DA-level unintentional injury hospitalization risk.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".