Health profiles of First Nations children living on-reserve in Northern Ontario: a pooled analysis of survey data
Bibliographic record
Abstract
BACKGROUND: The Aboriginal Children's Health and Well-Being Measure (ACHWM) was developed to enable Aboriginal health leaders to gather information on the health of children at a local community level. This paper aims to describe the typical health profiles of First Nation children living on traditional territory as a reference to assist in the interpretation of ACHWM scores. METHODS: Three First Nations in Ontario, Canada, gathered health data from children using the ACHWM administered on Android tablets between 2013 and 2015. The survey data were previously analyzed to inform local health planning. These survey data were pooled to describe the distribution of ACHWM summary and quadrant scores from a larger sample and inform interpretation of ACHWM scores. RESULTS: ACHWM data from 196 participants (aged 7.6 to 21.7 yr) across 3 communities were included in the pooled sample. ACHWM summary scores ranged from 39.8 to 98.7 with a mean of 74.1 (95% confidence interval [CI] 72.5-75.7) and a maximum of 100. Strengths were reported in the spiritual (mean 78.7, 95% CI 76.7-80.8), physical (mean 77.1, 95% CI 75.1-79.0) and emotional (mean 74.4, 95% CI 72.5-76.3) quadrants. The greatest opportunity for improvement was in the mental (cognition) quadrant (mean 61.6, 95% CI 56.9-63.4). INTERPRETATION: This paper presents initial estimates for child health scores based on self-report from a large sample of First Nations children living on reserve. These results establish benchmarks to aid interpretation of the ACHWM scores in these and other communities and contexts in the future.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".