Triple Jeopardy: Complexities of Racism, Sexism, and Ageism on the Experiences of Mental Health Stigma Among Young Canadian Black Women of Caribbean Descent
Bibliographic record
Abstract
This article explores how the intersection of race, gender, and age intertextually complicate and nuance the experience of mental health stigma among young Black women of Caribbean descent living in Canada. The Mental Health Commission of Canada acknowledged that mental health stigma continues to affect the help-seeking behavior of young adults. Some youth-serving agencies and many advocates within Black communities have become increasingly vocal about mental health stigma and the lackluster response to the needs of Black youth (e.g., no increase in funding for the Substance Abuse Program for African, Canadian, and Caribbean Youth-SAPACCY, since the program was established in the mid-1990s). The issue of mental health stigma within the African, Caribbean, and Black Canadian (ACB) communities is widely known and often discussed at public forums. Several recent mental health forums and mental health initiatives held in Toronto made it clear that mental health in Black communities is at a crisis point in the Greater Toronto Area (GTA) and possibly across Canada. Forum discussions also revealed that the issue is further compounded by the intersection of race, gender, and age. In addition, while research studies have also identified stigma as a barrier to accessing mental health services and/or supports, there is a paucity of research on how mental health stigma, when complicated by the experience of racism, sexism, and ageism, affects access to services among young Black women of Caribbean descent. This lack of research on Caribbean women's experience with mental illness limits insights into concepts, issues, and problems that directly impact broader issues related to mental health in Canada. This article engenders a discussion that strengthens the focus on mental health stigma campaigns and education on the mental health of young Black women in Canada. The lack of literature relating to this topic in the Canadian context, as previously noted, limits the extent to which this issue can be fully discussed within Canada. As such, insights into concepts and existing discussions on women's mental health throughout this paper will include references to literature from the U.S., U.K., and Australia, professional experiential knowledge, and personal insights from conversations with young Black women of Caribbean descent. The paper calls for more research on Caribbean women's mental health in Canada to provide better insights and understanding of the issue within a Canadian context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".