Are visible minorities “invisible” in Canadian health data and research? A scoping review
Bibliographic record
Abstract
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.
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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 | 0.068 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.052 | 0.066 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".