Exploring the Prevalence of Unmet Mental Health Needs Across Race and Immigration Status in Canada: A Comparative Analysis of the 2022 Mental Health and Access to Care Survey (MHACS)
Bibliographic record
Abstract
The rise of mental health issues in Canada underscores disparities in access to care, with many individuals avoiding or unable to obtain treatment due to social and economic barriers. Unmet mental health needs—cases where treatment is inadequate or not received—are particularly prevalent among racialized groups and immigrants. This study examines the impact of race and migration status on unmet mental health needs among individuals with poor/fair mental health or diagnosed mental health conditions. Using a modified Poisson regression, we assessed the magnitude of unmet mental health needs among Non-Racialized Foreign Born, Racialized Foreign Born, and Racialized Domestic Born individuals relative to Non-Racialized Domestic Born individuals, using data from the 2022 Mental Health and Access to Care Survey (MHACS). Racialized Foreign Born individuals had a higher magnitude of unmet needs compared to Non-Racialized Domestic Born individuals (PR: 1.12, CI:1.04-1.20). Efforts to de-stigmatize mental health care and improve accessibility are encouraged.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".