Obstetric care navigation: a new approach to promote respectful maternity care and overcome barriers to safe motherhood
Bibliographic record
Abstract
BACKGROUND: Disrespectful and abusive maternity care is a common and pervasive problem that disproportionately impacts marginalized women. By making mothers less likely to agree to facility-based delivery, it contributes to the unacceptably high rates of maternal mortality in low- and middle-income countries. Few programmatic approaches have been proposed to address disrespectful and abusive maternity care. OBSTETRIC CARE NAVIGATION: Care navigation was pioneered by the field of oncology to improve health outcomes of vulnerable populations and promote patient autonomy by providing linkages across a fragmented care continuum. Here we describe the novel application of the care navigation model to emergency obstetric referrals to hospitals for complicated home births in rural Guatemala. Care navigators offer women accompaniment and labor support intended to improve the care experience-for both patients and providers-and to decrease opposition to hospital-level obstetric care. Specific roles include deflecting mistreatment from hospital staff, improving provider communication through language and cultural interpretation, advocating for patients' right to informed consent, and protecting patients' dignity during the birthing process. Care navigators are specifically chosen and trained to gain the trust and respect of patients, traditional midwives, and biomedical providers. We describe an ongoing obstetric care navigator pilot program employing rapid-cycle quality improvement methods to quickly identify implementation successes and failures. This approach empowers frontline health workers to problem solve in real time and ensures the program is highly adaptable to local needs. CONCLUSION: Care navigation is a promising strategy to overcome the "humanistic barrier" to hospital delivery by mitigating disrespectful and abusive care. It offers a demand-side approach to undignified obstetric care that empowers the communities most impacted by the problem to lead the response. Results from an ongoing pilot program of obstetric care navigation will provide valuable feedback from patients on the impact of this approach and implementation lessons to facilitate replication in other settings.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".