Mental Health of Black Canadians: A Scoping Review
Bibliographic record
Abstract
Black Canadians frequently experience significant challenges when attempting to access mental health care, resulting in discrepancies in mental health outcomes. This article describes a scoping review that aimed to understand the range and nature of research conducted on the mental health of black Canadians and to identify the gaps in this literature. An established methodological framework guided the scoping review process. We searched research databases (CINAHL, Embase, Global Health, MEDLINE, PsycINFO, Scopus, Sociological Abstracts, and Web of Science) and grey literature sources for peer-reviewed articles and grey reports on the health of black Canadians. Of the 14 121 articles screened, 43 were included in the review. Our review found spiritual support, resilience, collective culture, and culturally congruent support as facilitators of positive mental health of black people in Canada, while stigmatization, misconceptions, low uptake of mental services, and difficulties accessing mental health services were the most significant barriers. Strategies for improving the mental health of black people in Canada center on social, emotional, and community support. Our findings indicate the need for black stakeholder involvement in awareness creation and knowledge improvement, which will help to dispel the myths and misconceptions about mental health in black populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".