Inequities in Mental Health Care Facing Racialized Immigrant Older Adults With Mental Disorders Despite Universal Coverage: A Population-Based Study in Canada
Bibliographic record
Abstract
OBJECTIVES: Contemporary immigration scholarship has typically treated immigrants with diverse racial backgrounds as a monolithic population. Knowledge gaps remain in understanding how racial and nativity inequities in mental health care intersect and unfold in midlife and old age. This study aims to examine the joint impact of race, migration, and old age in shaping mental health treatment. METHODS: Pooled data were obtained from the Canadian Community Health Survey (2015-2018) and restricted to respondents (aged ≥45 years) with mood or anxiety disorders (n = 9,099). Multivariable logistic regression was performed to estimate associations between race-migration nexus and past-year mental health consultations (MHC). Classification and regression tree (CART) analysis was applied to identify intersecting determinants of MHC. RESULTS: Compared to Canadian-born Whites, racialized immigrants had greater mental health needs: poor/fair self-rated mental health (odds ratio [OR] = 2.23, 99% confidence interval [CI]: 1.67-2.99), perceived life stressful (OR = 1.49, 99% CI: 1.14-1.95), psychiatric comorbidity (OR = 1.42, 99% CI: 1.06-1.89), and unmet needs for care (OR = 2.02, 99% CI: 1.36-3.02); in sharp contrast, they were less likely to access mental health services across most indicators: overall past-year MHC (OR = 0.54, 99% CI: 0.41-0.71) and consultations with family doctors (OR = 0.67, 99% CI: 0.50-0.89), psychologists (OR = 0.54, 99% CI: 0.33-0.87), and social workers (OR = 0.37, 99% CI: 0.21-0.65), with the exception of psychiatrist visits (p = .324). The CART algorithm identifies three groups at risk of MHC service underuse: racialized immigrants aged ≥55 years, immigrants without high school diplomas, and linguistic minorities who were home renters. DISCUSSION: To safeguard health care equity for medically underserved communities in Canada, multisectoral efforts need to guarantee culturally responsive mental health care, multilingual services, and affordable housing for racialized immigrant older adults with mental disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 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".