Antiracist training programs for mental health professionals: A scoping review
Bibliographic record
Abstract
Racism has been shown to be directly deleterious to the mental health care received by minoritized peoples. In response, some mental health institutions have pledged to provide antiracist mental health care, which includes training mental health care professionals in this approach. This scoping review aimed to synthesize the existing published material on antiracist training programs among mental health care professionals. To identify studies, a comprehensive search strategy was developed and executed by a research librarian in October 2022 across seven databases (APA PsycInfo, Education Source, Embase, ERIC, MEDLINE, CINAHL, and Web of Science). Subject headings and keywords relating to antiracist training as well as to mental health professionals were used and combined. There were 7186 studies generated by the initial search and 377 by the update search, 30 were retained and included. Findings revealed four main antiracist competencies to develop in mental health professionals: importance of understanding the cultural, social, and historical context at the root of the mental health problems; developing awareness of individual biases, self-identity and privilege; recognizing oppressive and racism-sustaining behaviors in mental health care settings; and, employing antiracist competencies in therapy. Professionals who have taken trainings having the main components have developed skills on the interconnectedness between racialized groups' mental health and the cultural, religious, social, historical, economic, and political issues surrounding race, necessary for successful clinical practice and for providing anti-racist mental health care. This scoping review presents a summary of the essential antiracist competencies drawn from the literature which must be applied in a mental health care setting, to improve help seeking behaviors, and reduce distrust in mental health care professionals and settings.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".