Improving Maternity Care in India’s Private Hospitals: Quality Certification? Yes, but More Is Needed
Bibliographic record
Abstract
See related article by Marx Delaney et al.O ver the last decade, the focus in maternal and new- born health care has shifted from improving coverage of health care services toward ensuring that care provided through these services is of the best quality.Quality of care encompasses the provision of care as well as the experience of care. 1 Global standards for quality maternal and newborn care published by the World Health Organization 2 have been widely adopted by national governments.Many partnerships and networks have been formed to promote the quality-of-care agenda, particularly in low-and middle-income countries.3 Marx Delaney et al. report on a quality improvement initiative in private hospitals in India.4 Among the 24 million births in India every year, 94% of those in urban areas and 88% of those in rural areas take place in health care facilities.More women (65%) in rural areas than in urban areas (53%) give birth in public health care facilities.5 The remainder of institutional births take place in private hospitals, which are independently managed and less often subject to critical regulatory oversight.Manyata is a quality improvement and certification initiative offered by the Federation of Obstetric and Gynaecological Societies of India (FOGSI) for private facilities providing maternal care.6 FOGSI is an umbrella organization with more than 38,000 members spread through 263 societies across the country working mostly in private facilities.Manyata's vision is to "ensure all women have access to safer and respectful care during and after childbirth in India," and it aims to "improve quality of maternity and newborn care services in private facilities by training doctors, nursing and administrative staff on essential clinical, facility and patient care protocols."The Manyata initiative, which several national and international partners supported, was initially implemented in 3 states in India but is now being implemented in other states.It focuses on mentorship and clinical standards to improve health care providers' knowledge,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.041 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".