Inequities in quality perinatal care in the United States during pregnancy and birth after cesarean
Bibliographic record
Abstract
OBJECTIVE: High-quality, respectful maternity care has been identified as an important birth process and outcome. However, there are very few studies about experiences of care during a pregnancy and birth after a prior cesarean in the U.S. We describe quantitative findings related to quality of maternity care from a mixed methods study examining the experience of considering or seeking a vaginal birth after cesarean (VBAC) in the U.S. METHODS: Individuals with a history of cesarean and recent (≤ 5 years) subsequent birth were recruited through social media groups to complete an online questionnaire that included sociodemographic information, birth history, and validated measures of respectful maternity care (Mothers on Respect Index; MORi) and autonomy in maternity care (Mother's Autonomy in Decision Making Scale; MADM). RESULTS: Participants (N = 1711) representing all 50 states completed the questionnaire; 87% planned a vaginal birth after cesarean. The most socially-disadvantaged participants (those less educated, living in a low-income household, with Medicaid insurance, and those participants who identified as a racial or ethnic minority) and participants who had an obstetrician as their primary provider, a male provider, and those who did not have a doula were significantly overrepresented in the group who reported lower quality maternity care. In regression analyses, individuals identified as Black, Indigenous, and People of Color (BIPOC) were less likely to experience autonomy and respect compared to white participants. Participants with a midwife provider were more than 3.5 times more likely to experience high quality maternity care compared to those with an obstetrician. CONCLUSION: Findings highlight inequities in the quality of maternal and newborn care received by birthing people with marginalized identities in the U.S. They also indicate the importance of increasing access to midwifery care as a strategy for reducing inequalities in care and associated poor outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".