‘One woman, one bed’: prevalence and factors associated with women’s experiences of respectful birth in urban Dar es Salaam, Tanzania – across-sectional survey
Bibliographic record
Abstract
BACKGROUND: Respectful maternity care (RMC) is essential for quality care, safety, and a fundamental right of women during childbirth. However, mistreatment during childbirth hinders global efforts to reduce maternal and perinatal deaths and birth-related injuries. In rapidly urbanizing Dar es Salaam, disrespectful care in overcrowded maternity units is concerning. OBJECTIVE: To assess the prevalence and factors associated with women's experiences of RMC in four urban health facilities in Dar es Salaam. METHOD: A 25-item locally co-created and validated measurement tool was administered to 838 postnatal women before discharge in a cross-sectional survey. Data were analyzed in Stata 14 to describe sociodemographic characteristics, birth outcomes, and birth experiences. Multivariable logistic regression identified factors associated with RMC. RESULTS: Satisfaction was reported by 96.4% (793/823) of women. Additionally, 84.3% (689/817) reported effective communication. However, 60.8% (503/827) shared hospital beds, 32.2% (253/785) experienced mistreatment, and 10.7% (89/829) had a birth companion. RMC was significantly less frequent among single women (aOR 0.56; 95% CI: 0.36-0.87) and those with childbirth complications (aOR 0.52; 95% CI: 0.35-0.78). Complications were reported less frequently when women had their own bed (aOR 0.51; 95% CI: 0.34-0.77). CONCLUSION: High satisfaction scores, despite mistreatment, bed-sharing, and lack of birth companionship highlight the need to raise awareness of rights-based care in communities. As urban growth strains healthcare systems, addressing structural constraints and overcrowding is crucial. Strengthening provider training in RMC and complications management, along with institutionalizing RMC measurements, can improve accountability, clinical outcomes, and women's experiences of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".