Seeing both sides: detailing the experiences of Black women mental healthcare workers serving Black youth
Bibliographic record
Abstract
Abstract Background The impacts of racial discrimination experienced by Black people in the United States and Canada have received a renewed focus. Within the mental healthcare sector, there has been a coinciding focus on how racism within and outside of mental healthcare organizations affects the mental health and wellbeing of Black youth. However, little attention has focused on the experiences of the mental healthcare workers best equipped to care for them: Black mental healthcare workers, in particular, women. Thus, the aim of this article is to 1) introduce and define Black Sistahood in Canada, 2) elucidate how anti-Black racism, sexism and other forms of oppression affect Black women, and 3) draw links to implications for the sector and the care of Black youth. Methods Focus groups (n = 7) with mental health service providers (n = 35) in six regions in the province of Ontario, Canada: The Greater Toronto Area (GTA), Ottawa, Hamilton, Kitchener-Waterloo, London, and Windsor. Focusing on participants who self-identified as Black or mixed-race (n = 24). Results Racism in the workplace created challenges for Black women mental healthcare workers. Three themes emerged: 1) Black women mental healthcare workers are subject to a toxic work environment, 2) Black women taking on multiple roles and 3) Impact on the profession and Black youth. Conclusions Racism and discrimination impact the career trajectories of Black women employed in the mental healthcare sector. Due to this, Black women continue to leave the profession, which has a deleterious impact on the sector and care of Black youth.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".