Culturally Responsive Services as a Path to Equity in Mental Healthcare
Bibliographic record
Abstract
Indigenous peoples, immigrants and refugees and racialized groups, as well as some long-established ethnic, linguistic, cultural and religious communities, experience inequities in mental health in Canadian society. These inequities result from social structural determinants of health that are embedded in the cultural knowledge, values and attitudes of the specific group as well as those of the larger society. Culture shapes the experience and expression of mental health problems, modes of coping, pathways to care and the effectiveness of treatment and prevention, as well as the processes of resilience and recovery. Systematic attention to culture in the provision of mental health services can improve access, utilization and health outcomes. We review models to address diversity in mental healthcare and identify key areas in which we believe policy innovation is urgently needed: 1. Cultural competence, safety and anti-racism training and accreditation standards for practitioners and for healthcare education, service systems and institutions; 2. National regulations and quality assurance standards to ensure use of language interpreters; 3. Development of a cadre of culture brokers to improve clinical communication; and 4. Integration of attention to culture in service systems design, as well as clinical practice.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".