Ancestry or identity? The importance of Indigenous engagement in articulating First Nations, Inuit and Métis peoples in the 2006 Canadian Census Health and Environmental Cohort (CanCHEC)
Bibliographic record
Abstract
Statistics Canada uses two self-report measures - Ancestry and Identity - in the Canadian Census to identify First Nations, Inuit and Métis (FNIM) peoples. How these measures are employed alone or in combination to assess definitional impact on the reporting of health conditions has not been investigated. To illustrate, we assessed how these measures, alone or in combination, estimate colorectal cancer rates. A working group comprised of Indigenous and non-Indigenous academics assessed the response patterns to the Identity and Ancestry questions in the 2006 Canadian Census Health and Environment Cohort and categorised the responses into groups: A) Identity only; B) Ancestry only; C) any Ancestry or Identity; D) both Ancestry and Identity. We then assessed concordance, and subsequently examined the way these groupings may impact the reporting of colorectal cancer rates (2010-2015). FNIM responses varied across the different combinations of the Ancestry and Identity questions. Concordance for FNIM was 76%, 81%, and 18% respectively for single responses, which impacted the estimation of colorectal cancer rates. To improve health reporting, it is essential that research teams choose the most appropriate definition in partnership with FNIM and urban Indigenous organisations to ensure the right data are analysed to align with community priorities.
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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 | 0.021 | 0.055 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".