Examining Child Health and Health Care Support in a Group of Off-Reserve Canadian Indigenous Children Diagnosed with Epilepsy
Bibliographic record
Abstract
In comparison to children with epilepsy in the general population, little is known about the profile and outcomes of Indigenous children with epilepsy. The 2006 Aboriginal Childrens Survey (ACS) was used to examine risk and resiliency factors in a sample of Indigenous children between one and five years of age. Logistic regressions were completed on a subset of the ACS population (epilepsy group N = 600; Control group N = 5890), where children were matched according to age, sex, and health status. \nIndigenous children in Canada had higher rates of epilepsy compared to the overall rate of epilepsy for children in Canada. Children with epilepsy compared to those without epilepsy had significantly higher rates of vision and hearing issues, allergies, asthma/bronchitis, and speech-language difficulties. Children with epilepsy were less likely to see a specialist than those without epilepsy. Children who received breast milk were significantly less likely to have epilepsy than those children who did not receive breast milk. If a child had a medical, neurodevelopmental or mental disorder, they were more likely to have epilepsy. Caregivers who rated themselves as healthy were less likely to have a child with epilepsy; even when comparing children with poor health status those caregivers who were healthy had less chance of having a child with epilepsy. In addition to caregiver health, those caregivers who were removed from the home as children were also more likely to have children with epilepsy regardless of the childs health status. \nCanada will continue to face challenges in providing care to Indigenous children, unless it addresses some important gaps in how health care is provided to this vulnerable population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".