Canadian data sources on ethnic classifications: Contemporary and historical developments in heterogeneity
Bibliographic record
Abstract
ABSTRACTObjectiveA thorough understanding of the health status of Canadians must take into account their ethnicity, given the genetic and social effects of race and ethnicity on health. Our objective is to describe Canadian data sources that collect ethnicity data and the degree of granularity that exists for ethnic classifications within these sources. We contextualize changes to the collection of ethnic data by considering their historical, social, and political circumstances. ApproachOur methods are informed by environmental scan and history methodology. We searched publicly available government documents, peer-reviewed literature, and contacted key informants to gain a comprehensive understanding of the Canadian sources available for collecting nationally representative ethnicity data. Two investigators, using qualitative content analysis, analyzed these sources independently. We extracted information form sources relating to the ethnic classifications (e.g., race, ethnic origins, colour, ancestry) and constructed a historical timeline of key changes. We mapped these to Canada’s changing social and political landscape and drew on contemporary literature to consider the implications of these changes for population health. The study team met to discuss findings, interpretations, and themes that emerged from these sources. ResultsThere are four main sources of ethnicity data in Canada used for health research: 1) Provincial health insurance registries, 2) Canadian Health Measures Survey, 3) Canadian Community Health Survey, 4) Census. Of these, ethnicity data are most limited in the provincial health insurance registries, flagging only Aboriginal status. The other three data sources are nationally administered, with all asking individuals to select, out of 11 categories, self-identified racial or ethnic groups. Historically, Canada’s changing policies on multiculturalism and immigration have influenced the collection of ethnic data to become more inclusive and granular. Important periods include early attempts at nation-building during the late 19th century, social changes post-WWII and the introduction of multiculturalism into federal policy, and present-day efforts in ethnic classifications for research purposes and preserving cultural diversity. ConclusionsThere is a need for greater granularity in ethnic classifications to reflect the diversity of the Canadian population. Consideration should be made to capture ethnicity as a social, cultural, and historical concept concept, especially in large data sources that influence health decision-making, such as the census. For example, questions on ethnic classification may consider incorporating questions about sense of belonging with the identified ethnic ancestry, rather than relying solely on reported ethnic origin and race.
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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 | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.075 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".