An analysis of the Mental Health of Black Canadians Fund: facilitators of success, challenges and recommendations
Bibliographic record
Abstract
INTRODUCTION: In 2018, in an effort to address the mental health inequities experienced by Black Canadians, the Government of Canada announced a CAD 10 million investment to establish the Public Health Agency of Canada's Promoting Health Equity: Mental Health of Black Canadians Fund (MHBC). The aim of this study was to examine and document the lessons learned from the MHBC, including successes and challenges. METHODS: Researchers conducted document analysis of 15 participating projects from 14 organizations' annual and final reports. Researchers then conducted interviews with representatives from nine of these organizations. An embedded case study design was used in the data collection and data analysis that included content analysis of annual and final reports, as well as thematic analysis of individual interviews. RESULTS: Analysis of the data from annual and final reports and interviews illuminated three main themes: facilitators of successes; challenges; and lessons learned and recommendations for funders. Facilitators included honorariums and incentives, participatory action research design and Black leadership. Challenges included delays (for obtaining ethics approval and program implementation); impacts of the COVID-19 pandemic; and difficulties maintaining partnerships. Finally, the lessons learned and recommendations that emerged for funders were that there is a need for longer term and more flexible funding, more Black representation and leadership within funding organizations and greater support of antiracist practices among mainstream service providers. CONCLUSION: The findings of this study present the challenges and opportunities in supporting work aimed at improving the mental health and well-being of Black people in Canada.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".