Chronic disease and chronic disease risk factors among First Nations, Inuit and Métis populations of northern Canada
Bibliographic record
Abstract
INTRODUCTION: Aboriginal populations in northern Canada are experiencing rapid changes in their environments, which may negatively impact on health status. The purpose of our study was to compare chronic conditions and risk factors in northern Aboriginal populations, including First Nations (FN), Inuit and Métis populations, and northern non-Aboriginal populations. METHODS: Data were from the Canadian Community Health Survey for the period from 2005 to 2008. Weighted multiple logistic regression models tested the association between ethnic groups and health outcomes. Model covariates were age, sex, territory of residence, education and income. Odds ratios (ORs) are reported and a bootstrap method calculated 95% confidence intervals (CIs) and p values. RESULTS: Odds of having at least one chronic condition was significantly lower for the Inuit (OR = 0.59; 95% CI: 0.43-0.81) than for non-Aboriginal population, but similar among FN, Métis and non-Aboriginal populations. Prevalence of many risk factors was significantly different for Inuit, FN and Métis populations. CONCLUSION: Aboriginal populations in Canada's north have heterogeneous health status. Continued chronic disease and risk factor surveillance will be important to monitor changes over time and to evaluate the impact of public health interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".