Navigating medical racism : how Black Canadian women experience reproductive healthcare in Canada
Bibliographic record
Abstract
Black women in the United States die of pregnancy-related causes at a three times higher rate than white women, regardless of the mother’s socioeconomic status. Systemic medical racism contributes to high morbidity and mortality rates among Black populations. Currently, there is a lack of race-based data collected in Canada. Given there is evidence of medical racism experienced by Black women in the United States, Black Canadian women may experience medical racism within the Canadian healthcare system. This study aims to understand how Black Canadian women navigate and manage obstetric racism in reproductive healthcare contexts. Trauma-informed intersectional feminist research practices empower and encourage Black women to share their experiences. Qualitative descriptive was used to understand how Black women experience medical racism. Black women living in Canada actively participated in twenty-five in-depth one-on-one interviews. Five themes include: 1) Black women anticipate experiencing some form of discrimination given the known health inequalities in the U.S., 2) healthcare providers consistently dismiss Black women’s pain. 3) healthcare providers’ perception of Black women affects their care, 4) increased need for Black representation in reproductive healthcare providers, and 5) patient-centred care improves Black women’s wellbeing. This study findings show that, despite implementing strategies to overcome racism and discrimination, participants still endure forms of systemic racism. Healthcare providers need to be aware that Black women experience health disparities due to a providers’ personal bias and the systemic barriers that exist in the healthcare system. Healthcare providers must understand that Black women may withhold health information to protect themselves from negative stereotypes. Healthcare providers need training on cultural safety to recognize their implicit biases and to provide safe and equitable care. Recruiting medical professionals who reflect racially underrepresented backgrounds will help to address systemic barriers in healthcare. The evidence suggests that Black Canadian women experience medical obstetric racism even with a successful pregnancy and delivery. The Canadian healthcare system must be aware of these issues and advocate for Black Canadian women to reduce instances of medical racism.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".