Cross-Canada Forum – How we identify and count Aboriginal people—does it make a difference in estimating their disease burden?
Bibliographic record
Abstract
Introduction We examined the concordance between the Canadian Community Health Survey (CCHS) ''identity'' and ''ancestry'' questions used to estimate the size of the Aboriginal population in Canada and whether the different definitions affect the prevalence of selected chronic diseases. Methods Based on responses to the ''identity'' and ''ancestry'' questions in the CCHS combined 2009–2010 microdata file, Aboriginal participants were divided into 4 groups: identity only; ancestry only; either ancestry or identity; and both ancestry and identity. Prevalence of diabetes, arthritis and hypertension was estimated based on participants reporting that a health professional had told them that they have the condition(s). Results Of participants who identified themselves as Aboriginal, only 63% reported having an Aboriginal ancestor; of those who claimed Aboriginal ancestry, only 57% identified themselves as Aboriginal. The lack of concordance also differs according to whether the individual was First Nation, Métis or Inuit. The different method of estimating the Aboriginal population, however, does not significantly affect the prevalence of the three selected chronic diseases. Conclusion The lack of concordance requires further investigation by combining more cycles of CCHS to compare discrepancy across regions, genders and socio-economic status. Its impact on a broader list of health conditions should be examined.
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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 | 0.032 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".