Obstetric racism and perceived quality of maternity care in Canada: Voices of Black women
Bibliographic record
Abstract
BACKGROUND: Obstetric racism in healthcare encounters impact on access to quality maternal healthcare for Black childbearing women yet remains underexplored in Canada. Understanding the experiences of Black Canadian women is critical to inform policy and create targeted interventions to address obstetric racism and advanced maternal health equity. OBJECTIVE: The aim of this study was to explore the experiences of obstetric racism and its influence on perceived quality of maternity care among Black women in Toronto, Canada. DESIGN: Qualitative research was conducted using a critical qualitative inquiry approach. METHODS: We conducted a semi-structured interviews with 24 Black women who were pregnant and/or have given birth in the last 3 years. The interviews explored their experiences seeking care during pregnancy/childbirth and perceived quality of care. RESULTS: Two themes were generated through the process of thematic analysis: (1) Manifestations and Impacts of Obstetric Racism and (2) Strategies for Addressing Obstetric Racism. Narratives of being dismissed, objectified, dehumanized, trauma and paternalism were reflected in the accounts of the participants. These experiences undermined the quality of care, hindered therapeutic relationships and contributed to mistrust. CONCLUSION: Black women understood the nature and impact of obstetric racism as it relates to the quality of maternal health care, their safety, and well-being. Participants recommended the need for anti-Black racism training specific to caring of Black childbearing women and increasing Black healthcare provider representation in perinatal settings as strategies to address obstetric racism. Investment in Black maternal health research is urgently needed to generate meaningful evidence to inform policy and interventions to advanced maternal health equity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".