Preferences for formal and traditional sources of childbirth and postnatal care among women in rural Africa: A systematic review
Bibliographic record
Abstract
BACKGROUND: The underutilization of formal, evidence-based maternal health services continues to contribute to poor maternal outcomes among women living in rural Africa. Women's choice of the type of maternal care they receive strongly influences their utilization of maternal health services. There is therefore a need to understand rural women's preferred choices to help set priorities for initiatives attempting to make formal maternal care more responsive to women's needs. The aim of this review was to explore and identify women's preferences for different sources of childbirth and postnatal care and the factors that contribute to these preferences. METHODS: A systematic literature search was conducted using the Ovid Medline, Embase, CINAHL, and Global Health databases. Thirty-seven studies that elicited women's preferences for childbirth and postnatal care using qualitative methods were included in the review. A narrative synthesis was conducted to collate study findings and to report on patterns identified across findings. RESULTS: During the intrapartum period, preferences varied across communities, with some studies reporting preferences for traditional childbirth with traditional care-takers, and others reporting preferences for a formal facility-based childbirth with health professionals. During the postpartum period, the majority of relevant studies reported a preference for traditional postnatal services involving traditional rituals and customs. The factors that influenced the reported preferences were related to the perceived need for formal or traditional care providers, accessibility to maternal care, and cultural and religious norms. CONCLUSION: Review findings identified a variety of preferences for sources of maternal care from intrapartum to postpartum. Future interventions aiming to improve access and utilization of evidence-based maternal healthcare services across rural Africa should first identify major challenges and priority needs of target populations and communities through formative research. Evidence-based services that meet rural women's specific needs and expectations will increase the utilization of formal care and ultimately improve maternal outcomes across rural Africa.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".