Addressing the Stigma of Mental Illness in Black Families and Communities in Ontario, Canada: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Racism and discrimination are among the factors perpetuating the persistent disparities within the Canadian health sector and related social and community services. Addressing issues of racism in Canada is crucial to reducing the mounting mental health disparities that subsequently impact the psychological well-being of diverse groups of people, particularly racialized and Black individuals. While some research has been conducted on mental illness-related stigma, very few peer-reviewed studies have attempted antistigma interventions to address mental health disparities in Black families and communities in Canada. OBJECTIVE: This study aims to generate critical knowledge to reduce mental health disparities and mental illness stigma experienced by Black families and communities and engage them in cocreating a best-practice model to guide policy and programming. Our study intends to engage individuals living with or affected by mental illness, service providers, and community leaders in Black communities who are interested in stigma reduction activities and advocacy in Ontario, particularly in the Greater Toronto Area (GTA), including Durham and York Regions, London, Ontario, Brampton, and Ottawa. METHODS: Informed by population health promotion approaches, critical race theory, and an intersectionality framework underpinned by social justice principles, this mixed methods study will engage individuals of Caribbean and African descent in 5 cities in Ontario. We will use online self-reported surveys with Black individuals (335/431) to assess depression, anxiety, stress, mental health knowledge, racial discrimination, and mental health stigma. We will also engage Black individuals (40/431) and service providers and community leaders (16/431) in focus groups and individual interviews (10/431). Results from the survey and focus groups will inform concept mapping activities with cross-sector leaders, decision makers, and community advocates (30/431) to cocreate a best-practice model to improve mental health outcomes in Black families and communities. Quantitative data will be analyzed using descriptive and inferential analyses through SPSS (IBM Corp). Qualitative data will be transcribed verbatim, and NVivo software (Lumivero) will be used for data management. We will apply Braun and Clarke's framework of 6 phases in thematic analysis. RESULTS: As of September 2024, the study has received ethical approval in Canada. We have completed data collection for phase one of the study and plans are far advanced to start recruitment for phases 2 and 3. Results from the study are expected in the last quarter of 2025 and the first quarter of 2026. CONCLUSIONS: This project will generate a novelty of knowledge to contribute to effective ways of addressing mental illness stigma and promoting mental health literacy in Black families and communities and other vulnerable populations. In addition, the knowledge gained from this study will be taken back to Black communities to empower affected individuals and their families. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66851.
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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 | 0.051 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.077 | 0.009 |
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".