Chronic Care for All? The Intersecting Roles of Race and Immigration in Shaping Multimorbidity, Primary Care Coordination, and Unmet Health Care Needs Among Older Canadians
Bibliographic record
Abstract
OBJECTIVES: Despite the predominance of chronic disease clustering, primary care delivery for multimorbid patients tends to be less effective and often uncoordinated. This study aims to quantify racial-nativity inequalities in multimorbidity prevalence ≥3 chronic conditions), access to primary care, and relations to past-year subjective unmet health care needs (SUN) among older Canadians. METHODS: Population-based data were drawn from the Canadian Community Health Survey (2015-2018). Multivariable logistic regression was performed to estimate the likelihood of multimorbidity, sites of usual source of primary care (USOC), primary care coordination, and multidimensional aspects of SUN. The Classification and Regression Tree (CART) was applied to identify intersecting determinants of SUN. RESULTS: The overall sample (n = 19,020) were predominantly (69.4%) Canadian-born (CB) Whites (1% CB non-Whites, 18.1% White immigrants, and 11.5% racialized immigrants). Compared with CB Whites, racialized immigrants were more likely to have multimorbidity (adjusted odds ratio [AOR] = 1.35, 99% confidence interval [CI]: 1.13-1.61), lack a USOC (AOR = 1.41, 99% CI: 1.07-1.84), and report higher SUN (AOR = 1.47, 99% CI: 1.02-2.11). Racialized immigrants' greater SUN was driven by heightened affordability barriers (AOR = 4.31, 99% CI: 2.02-9.16), acceptability barriers (AOR = 3.11, 99% CI: 1.90-5.10), and unmet needs for chronic care (AOR = 2.71, 99% CI: 1.53-4.80) than CB Whites. The CART analysis found that the racial-nativity gap in SUN perception was still evident even among those who had access to nonpoorly coordinated care. DISCUSSION: To achieve an equitable chronic care system, efforts need to tackle affordability barriers, improve service acceptability, minimize service fragmentation, and reallocate treatment resources to underserved older racialized immigrants in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".