Antiracist mental healthcare training and development of multicultural awareness, knowledge and skills among school mental health providers in Canada
Bibliographic record
Abstract
In Canada, mental health disparities persist among racialized populations, including Black, Indigenous and other people of color. A major barrier to equitable care is the lack of adequate training of mental health professionals on racial issues. To address this unmet need, Cénat and colleagues introduced the Providing Antiracist Mental Health Care online training course. This contains five modules addressing: (a) awareness of racial issues, (b) assessment adapted to the needs of racialized individuals, (c) a humanistic approach to medication management, (d) treatment approaches for issues related to racism, and (e) providing tailored antiracist mental healthcare to children, adolescents, and families from racialized communities. This article discusses the implementation of this training among mental health providers in a school board in Ontario, Canada ( n = 27), assessing changes in participants’ multicultural awareness, knowledge, and skills at pre-, post-, and follow-up timepoints. The results show the ability of the Providing Antiracist Mental Health Care training course to effectively enhance multicultural awareness ( F (2, 21) = 10.52, p < .001), knowledge ( F (2, 21) = 11.88, p < .001) and skills ( F (2, 21) = 5.21, p = .014) among mental health providers in ethnically diverse schools. The total score improved significantly ( F (2, 20) = 12.17, p < .001) from pre-test to post-test, and follow-up, and no significant decrement from the post-test to follow-up was observed ( M = 2.81, SD = 0.16; p = .861). This study brings evidence supporting the need for sustained and comprehensive antiracist training initiatives to foster racial equity in mental health and improve outcomes in care for racialized individuals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".