Bearing witness: United States and Canadian maternity support workers’ observations of disrespectful care in childbirth
Bibliographic record
Abstract
BACKGROUND: Disrespectful care and abuse during childbirth are acknowledged global indicators of poor quality care. This study aimed to compare birth doulas' and labor and delivery nurses' reports of witnessing disrespectful care in the United States and Canada. METHODS: Maternity Support Survey data (2781 respondents) were used to investigate doulas' and nurses' reports of witnessing six types of disrespectful care. Multivariate analysis was conducted to examine the effects of demographics, practice characteristics, region, and hospital policies on witnessing disrespectful care. RESULTS: Nearly two-thirds of respondents reported witnessing providers occasionally or often engaging in procedures without giving a woman time or option to consider them. One-fifth reported witnessing providers occasionally or often engaging in procedures explicitly against the patient's wishes, and nurses were more likely to report witnessing this than doulas. Doulas and nurses who expected to leave their job within three years were significantly more likely to report that they witness most types of disrespectful care occasionally or often (OR 1.78-2.43). CONCLUSIONS: Doulas and nurses frequently said that they witnessed verbal abuse in the form of threats to the baby's life unless the woman agreed to a procedure, and failure to provide informed consent. Reports of witnessing some types of disrespectful care in childbirth were relatively uncommon among respondents, but witnessing disrespectful care was associated with an increased likelihood to leave maternity support work within three years, raising implications for the sustainability of doula practice, nursing work force shortages, and quality of maternity care overall.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".