Canadian data sources on ethnic classifications: Contemporary and historical developments in heterogeneity
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,007 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».