Are visible minorities “invisible” in Canadian health data and research? A scoping review
Notice bibliographique
Résumé
Purpose Canada’s visible minority population is increasing rapidly, yet despite the demographic significance of this population, there is a surprising dearth of nationally representative health data on visible minorities. This is a major challenge to undertaking research on the health of this group, particularly in the context of investigating racial/ethnic disparities and health disadvantages that are rooted in racialization. The purpose of this paper is to summarize: mortality and morbidity patterns for visible minorities; determinants of visible minority health; health status and determinants of the health of visible minority older adults (VMOA); and promising data sources that may be used to examine visible minority health in future research. Design/methodology/approach A scoping review of 99 studies or publications published between 1978 and 2014 (abstracts of 72 and full articles of 27) was conducted to summarize data and research findings on visible minority health to answer four specific questions: what is known about the morbidity and mortality patterns of visible minorities relative to white Canadians? What is known about the determinants of visible minority health? What is known about the health status of VMOA, a growing segment of Canada’s aging population, and how does this compare with white older adults? And finally, what data sources have been used to study visible minority health? Findings There is indeed a major gap in health data and research on visible minorities in Canada. Further, many studies failed to distinguish between immigrants and Canadian-born visible minorities, thus conflating effects of racial status with those of immigrant status on health. The VMOA population is even more invisible in health data and research. The most promising data set appears to be the Canadian Community Health Survey (CCHS). Originality/value This paper makes an important contribution by providing a comprehensive overview of the nature, extent, and range of data and research available on the health of visible minorities in Canada. The authors make two key recommendations: first, over-sampling visible minorities in standard health surveys such as the CCHS, or conducting targeted health surveys of visible minorities. Surveys should collect information on key socio-demographic characteristics such as nativity, ethnic origin, socioeconomic status, and age-at-arrival for immigrants. Second, researchers should consider an intersectionality approach that takes into account the multiple factors that may affect a visible minority person’s health, including the role of discrimination based on racial status, immigrant characteristics for foreign-born visible minorities, age and the role of ageism for older adults, socioeconomic status, gender (for visible minority women), and geographic place or residence in their analyses.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».