Harm at the Hands of the Healer: Narratives of Mistreatment and Coercion in Maternity Care: An Interpretive Description Qualitative Analysis
Bibliographic record
Abstract
Objective: To describe and classify mistreatment during maternity care as described by a diverse set of women across the United States. Design: Interpretive Description Qualitative Analysis Setting: Qualitative data were collected via a web-based survey (n=1151) and in semi-structured interviews (n=25). Sample: Adult women with a history of cesarean who had a subsequent birth (of any mode) in the United States in the 5 years preceding study participation. Methods: Deductive Content Analysis was employed using a priori codes based on Bohren et al.’s Typology of Mistreatment of Women during Childbirth framework. Results: Participants described all eight types of mistreatment. Participants with marginalized identities and socioeconomic disadvantage were more likely to describe mistreatment. Consequences of mistreatment in maternity care described by participants included healthcare system distrust, reduced postpartum healthcare utilization, and maternal mental health complications. Conclusions: Those most at risk for adverse maternal and infant birth outcomes were the most likely to describe mistreatment in their maternity care. In addition to inflicting birth-related trauma, this created a distrust of the healthcare system, decreased postpartum health care utilization, and resulted in missed opportunities for postpartum screening and follow-up for those at greatest risk of maternal mortality, severe morbidity, and postpartum mental health complications.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".