The Mistreatment of Women during Childbirth in Health Facilities Globally: A Mixed-Methods Systematic Review
Bibliographic record
Abstract
BACKGROUND: Despite growing recognition of neglectful, abusive, and disrespectful treatment of women during childbirth in health facilities, there is no consensus at a global level on how these occurrences are defined and measured. This mixed-methods systematic review aims to synthesize qualitative and quantitative evidence on the mistreatment of women during childbirth in health facilities to inform the development of an evidence-based typology of the phenomenon. METHODS AND FINDINGS: We searched PubMed, CINAHL, and Embase databases and grey literature using a predetermined search strategy to identify qualitative, quantitative, and mixed-methods studies on the mistreatment of women during childbirth across all geographical and income-level settings. We used a thematic synthesis approach to synthesize the qualitative evidence and assessed the confidence in the qualitative review findings using the CERQual approach. In total, 65 studies were included from 34 countries. Qualitative findings were organized under seven domains: (1) physical abuse, (2) sexual abuse, (3) verbal abuse, (4) stigma and discrimination, (5) failure to meet professional standards of care, (6) poor rapport between women and providers, and (7) health system conditions and constraints. Due to high heterogeneity of the quantitative data, we were unable to conduct a meta-analysis; instead, we present descriptions of study characteristics, outcome measures, and results. Additional themes identified in the quantitative studies are integrated into the typology. CONCLUSIONS: This systematic review presents a comprehensive, evidence-based typology of the mistreatment of women during childbirth in health facilities, and demonstrates that mistreatment can occur at the level of interaction between the woman and provider, as well as through systemic failures at the health facility and health system levels. We propose this typology be adopted to describe the phenomenon and be used to develop measurement tools and inform future research, programs, and interventions.
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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 | 0.030 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".