“It seemed like she just wanted me to suffer”: Acts of obstetric racism and birthing rights violations against Black women
Bibliographic record
Abstract
Studies that examine obstetric violence and mistreatment during perinatal care demonstrate that Black women experience higher levels of harm and abuse than other racialized groups. Yet these gender-based concepts do not fully recognize the intersectional gender-and race-based harms that Black women experience within the context of quality, safety, and human rights violations in the U.S. healthcare system. We performed qualitative secondary analysis from Black women participants in the Giving Voice to Mothers (GVtM) study (n = 304). Primary data collection for the GVtM survey spanned from 2016 to 2017, and our analysis occurred in 2023, focusing on the interpretation of open-ended responses to three categories of inquiry: worst experiences with perinatal care, experiences of being pressured to undergo medical interventions, and desired revisions to birthing experiences. We employed a deductive approach and applied two analytic frameworks – obstetric racism and the Black Birthing Bill of Rights (BBBR)– to categorize Black women's narratives of harm during perinatal care as quality, safety, and human rights violations. Black women described perinatal care experiences with considerable violations of the BBBR, including disrupted time with babies, racially discordant care, and unaffordable care. These experiences illustrated all six domains of obstetric racism. This study contributes to an emerging body of Black feminist approaches to knowledge production in obstetric patient safety, emphasizing the critical intersection of gender and race. Furthermore, this study underscores the value of using Black-women-defined frameworks with typologies to interpret the distinct experiences of Black women instead of the more limited gender-based concepts of obstetric violence, mistreatment, and respectful maternity care that lack historical context and contemporary implications of anti-Black racism and misogynoir.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".