RACIALIZED IMMIGRANTS' ACCESS TO MENTAL HEALTHCARE SERVICES
Bibliographic record
Abstract
There are distinctions noted in mental healthcare service uptake based on immigration status among racialized immigrants in Canada. Research focused on understanding mental healthcare disparities in accessing care within this broad population group often attends to individual-level drivers, which detracts from how systemic factors play a role in producing disparities. Through a three project study, this dissertation aims to explore how access to mental healthcare services among this broad population is influenced by different factors. First, a scoping review aimed to identify barriers and facilitators encountered by racialized immigrants when accessing mental healthcare services across Canada. Second, a qualitative descriptive study explored 16 racialized immigrants’ experiences of accessing mental health services in Ontario, Canada. Third, a qualitative descriptive study centred on the perspectives of both 16 service users and 10 mental healthcare service providers to explore how the immigration and mental healthcare systems coalesce together and play a role in shaping access to services. The findings from all three studies demonstrate how individual and systemic-level factors produce certain inequities for racialized immigrants when accessing mental healthcare services in Ontario. Improving access to mental healthcare services for this broad population group requires attention to how service delivery exists and is shaped by macro-level factors. By highlighting legal status as a starting point for interrogation related to understanding disparities in access, a more nuanced understanding can be gained to pinpoint drivers contributing to the issue. There also needs to be an emphasis on situating how the existing mental healthcare infrastructure plays a role, specifically how access is mediated through one’s legal status. Racialized immigrants are not a monolithic group and therefore, development of equitable policies, programs, and service delivery related to mental health should account this complexity rather than a one-size-fits-all approach.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".