Pathways to Healing and Thriving: Culturally Responsive Mental Health Programs for Black Youth in Toronto
Bibliographic record
Abstract
Black youth face unique challenges stemming from constant exposure to systemic and cultural racism, discrimination, and lack of access to culturally responsive services meeting their needs, which significantly impacts their emotional well-being, career trajectories, and civic engagement. The research project explores the benefits of a culturally responsive program called Catharsis offered by the non-profit organization Generation Chosen, which focuses on supporting Black youth with their mental health, emotional intelligence, and civic engagement. Data was collected between December 2022 and April 2023. Surveys and focus groups were administered to Black youth aged 15 to 20 in Toronto, Canada, who attended programming in the Jane and Finch community known as a racialized under-resourced neighbourhood. Twenty-nine surveys and two focus groups were administered, totalling 55 respondents. Critical Race Theory (CRT) as a theoretical framework was applied to centre the lived experiences of the youth and listen to their concerns and ideas as counter-narratives. Thematic analysis and triangulation of the data indicated that culturally responsive, trauma-informed programming can enhance emotional intelligence and lead to better coping mechanisms to manage stress and cope with systemic barriers. Participants reported improved life skills and mental health by accessing culturally responsive mental health service providers and engaging with staff who had similar lived experiences who modelled vulnerability as a form of strength and maturity. The research contributes to filling in the research gap in the Canadian context around the importance of culturally responsive, trauma-informed programming for Black youth and how it can foster healthy identity development and larger community benefits. Keywords: trauma-informed, mental health, Black youth, culturally responsive, emotional intelligence
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".