Acute care hospitalization of Aboriginal children and youth.
Bibliographic record
Abstract
BACKGROUND: Research that has examined Aboriginal children's hospitalization rates at the national level has been limited to analyses of areas with large percentages of Aboriginal residents, rather than of Aboriginal individuals. This study uses linked census and administrative data to describe hospitalization patterns among children and youth aged 0 to 19, by Aboriginal identity, for all provinces and territories except Quebec. DATA AND METHODS: The 2006 Census was linked to the 2006/2007-to-2008/2009 Discharge Abstract Database, which contains hospital records from all acute care facilities (except Quebec). Hospital records were examined by Aboriginal identity, as reported to the census, according to International Classification of Diseases chapters based on "the most responsible diagnosis." Age-standardized hospitalization rates (ASHRs) were calculated per 100,000 population, and age-standardized rate ratios (RRs) were calculated for Aboriginal groups relative to non-Aboriginal people. RESULTS: ASHRs were consistently higher among Aboriginal children and youth relative to their non-Aboriginal counterparts; rates for children aged 0 to 9 were 1.4 to 1.8 times higher; for youth aged 10 to 19, 2.0 to 3.8 times higher. For all children aged 0 to 9, the leading cause of hospitalization was "diseases of the respiratory system," but RRs for Aboriginal children ranged from 1.7 to 2.5, compared with non-Aboriginal children. Disparities between Aboriginal and non-Aboriginal 10- to 19-year-olds were pronounced for injuries due to assaults (RRs from 4.8 to 10.0), self-inflicted injuries (RRs from 2.7 to 14.2), and pregnancy, childbirth and the puerperium (RRs from 4.1 to 9.8). INTERPRETATION: Additional research is needed to examine reasons for the disparities in hospitalization rates between Aboriginal and non-Aboriginal children and youth.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".