Healthy Ageing in a Foreign Land? Examining Health Care Inequities Faced by Older Racialized Immigrants in the Canadian Community Health Survey (2015-2018)
Bibliographic record
Abstract
Background: Canada's demographic landscape has transformed to be more multicultural and multi-ethnic, resulting from an increasing inflow of racialized immigrant populations from the Global South, made possible by the abolition of racially discriminatory immigration policy in the 1970s. Although there is a significant body of Canadian research examining immigrants’ access to care, contemporary immigration scholarship has typically examined immigrants as a monolithic population while the intersecting lens of “race” and “immigration” has been largely overlooked. The invisibility of racialization in immigrant research has been criticized for its European-centric worldview. Therefore, this three-paper dissertation aims to make racialized immigrants’ ageing experiences visible through a racial-nativity lens in health equity and health care equity. Methods: A combined dataset was generated to pool over four annual circles (2015-2018) from the Canadian Community Health Survey (CCHS). Statistical methods including Chi-square tests, binary logistic regression, multinomial logistic regression and classification and regression tree analysis (CART) were used. Results: Racialized immigrant older adults systematically bear an excess burden of compromised conditions in physical and mental health consequences, including undiagnosed depressive symptoms (Chapter 2, C#2), unrecognized depressive symptoms (Chapter 2, C#2), poor-fair self-rated mental health (C#3), perceived stressful life (C#3), co-morbidity of mood and anxiety disorders (C#3), poor-fair self-rated health (C#4), and multiple chronic conditions (C#4), compared to the dominant Canadian-born White populations. Moreover, racialized immigrant older adults experienced inequities in access to various types of care across different settings, including depression diagnosis (C#2), mental health consultations from family doctors, psychologists, and social workers (C#3), unmet healthcare needs due to affordability and acceptability barriers (C#4), and unmet needs for treatment of chronic physical conditions (C#4), compared to the dominant Canadian-born White populations. Conclusion: These systematic differences in health conditions and differential patterns of accessing necessary health care services, by race-nativity status, are judged to be unfair in a way that undermine Canada's mandate of universal health coverage. These health and health care inequities are also remediable which warrant interventions to address problems with provisions of health care resources to racialized immigrants at interpersonal, organizational, and policy levels.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".