“I was able to take it back”: Seeking VBAC after experiencing dehumanizing maternity care in a primary cesarean
Bibliographic record
Abstract
In this article, we present findings from a qualitative narrative analysis that examined the pregnancy, primary cesarean, and subsequent birth experiences of women in the United States. Using a maximal variation sampling strategy, we recruited participants via social media and networking to participate in semistructured interviews. Twenty-five women from diverse backgrounds and geographic locations across the U.S. participated, eight self-identified as racialized and seventeen as non-Hispanic, White. Data were analyzed iteratively using Clandinin and Connelly's approach to Narrative Inquiry. Across their narratives, participants described their experiences of maternity care that were either generally negative (dehumanizing care) or positive (humanized care). They further described how their experiences of dehumanizing or humanized care impacted their decision-making for subsequent births, mental health, relationships with the healthcare system, early parenting birth satisfaction, and family planning. Findings suggest that regardless of ultimate mode of birth, what was most important to women was how they are treated by their maternity care team. We suggest practice changes that may improve the experience of maternity care for primary cesarean and subsequent births, especially among those made marginal by systems of oppression.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".