The Giving Voice to Mothers Study: Measuring Respectful Maternity Care in the United States [18Q]
Bibliographic record
Abstract
INTRODUCTION: Adverse perinatal outcomes among immigrants, refugees, and women of colors in the US are well-documented and persist even when socio-economic background is considered. METHODS: Community members and researchers designed a survey to capture patient-oriented data on maternity care experiences among communities of color. Items included previously validated instruments including the Mothers Autonomy in Decision Making (MADM) scale, the Mothers on Respect index (MORi); and the Perceptions of Racism (PR) scale. The final instrument was content validated, piloted, and distributed across the US. Descriptive statistics describe access, experience, and outcomes; and regression analyses link MADM and MORi scale scores to respectful care and autonomy (adjusting for differences in sociodemographics, risk profile, type of provider, and place of birth). RESULTS: Of the total sample (N=2260), 37.3% were women of color (Black, Hispanic, Native, other), and 18% Medicaid recipients. Women of color had significantly lower MADM scores, and 20.5% were not satisfied with their role in decision making. Women with low MORi scores reported pressure by health professionals to accept interventions [(6.8%) epidurals, (15.8%) inductions, (11.1%) cesarean]. Reported discrimination due to a difference in opinion with providers was more common (17%) among women of color. MADM, MORi, and PR scores varied significantly by place of birth and type of provider. CONCLUSION: Persons of color in the US report receiving less respectful maternity care, and reduced access to options for physiologic birth care. Data suggest that type of provider or place of birth modulates outcomes, and institutional racism may be a contributing factor.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".