The Realities Of Schizophrenia For Black African Families Navigating Greater Toronto's Mental Health Services
Bibliographic record
Abstract
This is a qualitative phenomenological (Padgett, 2017) study of how Black African families with relatives diagnosed with schizophrenia navigate and experience mental health systems in the Greater Toronto Area. Data was collected through individual face-to-face interviews with 3 participants who are self-identified adult African family members or relatives of a person diagnosed with schizophrenia. The research draws on the theoretical approaches of Anti-Black Racism (Kumsa et al, 2014) and Anti-Black Sanism (Abdillahi, Meerai & Poole, 2014; Meerai, Abdillahi & Poole, 2016). Participants had an outlet to share their experiences, and a space to share ideas on program development and coping strategies. The findings of this study suggest that Black families need a space where Black and/or African individuals with mental health challenges can safely navigate and share their stories through storytelling, poetry and music, to name a few methods. There is a need for Black and/or African navigating resource services for Black and/or African immigrants diagnosed with mental health challenges and their families. We can conclude that there are a number of areas of research which require more exploration, including the social construction of Black and/or African immigrant families faced with mental health challenges pre- and post-diagnosis and how they navigate mental health systems. It is critical to promote the voices of Black and/or African individuals with mental health challenges and their families in research and practice because “you cannot know about us without us” (Morrow &Malcoe, 2017, p.132).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".