Revealing the shape of knowledge using an intersectionality lens: results of a scoping review on the health and health care of ethnocultural minority older adults
Bibliographic record
Abstract
ABSTRACT This paper uses an intersectionality theoretical lens to interrogate selected findings of a scoping review of published and grey literature on the health and health-care access of ethnocultural minority older adults. Our focus was on Canada and countries with similar immigrant populations and health-care systems. Approximately 3,300 source documents were reviewed covering the period 1980–2010: 816 met the eligibility criteria; 183 were Canadian. Summarised findings were presented to groups of older adults and care providers for critical review and discussion. Here we discuss the extent to which the literature accounts for the complexity of categories such as culture and ethnicity, recognises the compounding effects of multiple intersections of inequity that include social determinants of health as well as the specificities of immigration, and places the experience of those inequities within the context of systemic oppression. We found that Canada's two largest immigrant groups – Chinese and South Asians – had the highest representation in Canadian literature but, even for these groups, many topics remain unexplored and the heterogeneity within them is inadequately captured. Some qualitative literature, particularly in the health promotion and cultural competency domains, essentialises culture at the expense of other determinants and barriers, whereas the quantitative literature suffers from oversimplification of variables and their effects often due to the absence of proportionally representative data that captures the complexity of experience in minority groups.
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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 | 0.045 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".