Manifestations of Anti-Black Racism and Worry About Pregnancy and Birthing While Black: A Cross-sectional Secondary Analysis of Giving Voice to Mothers
Bibliographic record
Abstract
INTRODUCTION: Pregnancy and childbirth traditionally bring worry or a sense of anxiety and distress, particularly among Black women that face historical and contemporary anti-Black racism. We employed two frameworks to assess manifestations of anti-Black racism, structural racism and obstetric racism, as predictors of worry about pregnancy and birth within the Black reproducing community. METHODS: In a secondary cross-sectional analysis, we analyzed data from Black women in the Giving Voice to Mothers study who completed all relevant items (n = 260). We conducted descriptive analyses and logistic regression models to explore how worry about pregnancy and birth for the Black reproducing community varies with experiences of obstetric racism and different manifestations of structural racism. RESULTS: Approximately 71% of the sample worried about pregnancy and birth for themselves and their community. Black women who experienced obstetric racism were statistically significantly more likely to be worried about pregnancy and birth experiences compared to Black women who did not. Furthermore, when structural racism was manifested and measured as hidden resources, among Black women reporting fewer pregnancy and birthing care options for women of color, those who experienced obstetric racism during care were 15.6 times more likely to worry about pregnancy and birthing experiences than those who did not (OR 15.667; 95% CI 1.348-182.058). CONCLUSION: The findings demonstrate the complexity of racialized harm enacted against Black women during the perinatal period and underscore the ways in which obstetric racism and contexts of structural racism powerfully shape the meaning and subsequent emotional impact of pregnancy and birthing while Black.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".