Advancing quality and safety of perinatal services in India: opportunities for effective midwifery integration
Bibliographic record
Abstract
India has made significant progress in improving maternal and child health. However, there are persistent disparities in maternal and child morbidity and mortality in many communities. Mistreatment of women in childbirth and gender-based violence are common and reduce women's sense of safety. Recently, the Government of India committed to establishing a specialized midwifery cadre: Nurse Practitioners in Midwifery (NPMs). Integration of NPMs into the current health system has the potential to increase respectful maternity care, reduce unnecessary interventions, and improve resource allocation, ultimately improving maternal-newborn outcomes. To synthesize the evidence on effective midwifery integration, we conducted a desk review of peer-reviewed articles, reports and regulatory documents describing models of practice, organization of health services and lessons learned from other countries. We also interviewed key informants in India who described the current state of the healthcare system, opportunities, and anticipated challenges to establishing a new cadre of midwives. Using an intersectional feminist theoretical framework, we triangulated the findings from the desk review with interview data to identify levers for change and recommendations. Findings from the desk review highlight that benefits of midwifery on outcomes and experience link to models of midwifery care, and limited scope of practice and prohibitive practice settings are threats to successful integration. Interviews with key informants affirm the importance of meeting global standards for practice, education, inter-professional collaboration and midwifery leadership. Key informants noted that the expansion of respectful maternity care and improved outcomes will depend on the scope and model of practice for the cadre. Domains needing attention include building professional identity; creating a robust, sustainable education system; addressing existing inter-professional issues and strengthening referral and quality monitoring systems. Public and professional education on midwifery roles and scope of practice, improved regulatory conditions and enabling practice environments will be key to successful integration of midwives in India.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".