Improving the experience of facility-based delivery for vulnerable women through obstetric care navigation: a qualitative evaluation
Bibliographic record
Abstract
BACKGROUND: Global disparities in maternal mortality could be reduced by universal facility delivery. Yet, deficiencies in the quality of care prevent some mothers from seeking facility-based obstetric care. Obstetric care navigators (OCNs) are a new form of lay health workers that combine elements of continuous labor support and care navigation to promote obstetric referrals. Here we report qualitative results from the pilot OCN project implemented in Indigenous villages in the Guatemalan central highlands. METHODS: We conducted semi-structured interviews with 17 mothers who received OCN accompaniment and 13 staff-namely physicians, nurses, and social workers-of the main public hospital in the pilot's catchment area (Chimaltenango). Interviews queried OCN's impact on patient and hospital staff experience and understanding of intended OCN roles. Audiorecorded interviews were transcribed, coded, and underwent content analysis. RESULTS: Maternal fear of surgical intervention, disrespectful and abusive treatment, and linguistic barriers were principal deterrents of care seeking. Physicians and nurses reported cultural barriers, opposition from family, and inadequate hospital resources as challenges to providing care to Indigenous mothers. Patient and hospital staff identified four valuable services offered by OCNs: emotional support, patient advocacy, facilitation of patient-provider communication, and care coordination. While patients and most physicians felt that OCNs had an overwhelmingly positive impact, nurses felt their effort would be better directed toward traditional nursing tasks. CONCLUSIONS: Many barriers to maternity care exist for Indigenous mothers in Guatemala. OCNs can improve mothers' experiences in public hospitals and reduce limitations faced by providers. However, broader buy-in from hospital staff-especially nurses-appears critical to program success. Future research should focus on measuring the impact of obstetric care navigation on key clinical outcomes (cesarean delivery) and mothers' future care seeking behavior.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".