Assessment of facility readiness and provider preparedness for dealing with postpartum haemorrhage and pre-eclampsia/eclampsia in public and private health facilities of northern Karnataka, India: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The maternal mortality ratio in India has been declining over the past decade, but remains unacceptably high at 212 per 100,000 live births. Postpartum haemorrhage (PPH) and pre- eclampsia/eclampsia contribute to 40% of all maternal deaths. We assessed facility readiness and provider preparedness to deal with these two maternal complications in public and private health facilities of northern Karnataka state, south India. METHODS: We undertook a cross-sectional study of 131 primary health centres (PHCs) and 148 higher referral facilities (74 public and 74 private) in eight districts of the region. Facility infrastructure and providers' knowledge related to screening and management of complications were assessed using facility checklists and test cases, respectively. We also attempted an audit of case sheets to assess provider practice in the management of complications. Chi square tests were used for comparing proportions. RESULTS: 84.5% and 62.9% of all facilities had atleast one doctor and three nurses, respectively; only 13% of higher facilities had specialists. Magnesium sulphate, the drug of choice to control convulsions in eclampsia was available in 18% of PHCs, 48% of higher public facilities and 70% of private facilities. In response to the test case on eclampsia, 54.1% and 65.1% of providers would administer anti-hypertensives and magnesium sulphate, respectively; 24% would administer oxygen and only 18% would monitor for magnesium sulphate toxicity. For the test case on PPH, only 37.7% of the providers would assess for uterine tone, and 40% correctly defined early PPH. Specialists were better informed than the other cadres, and the differences were statistically significant. We experienced generally poor response rates for audits due to non-availability and non-maintenance of case sheets. CONCLUSIONS: Addressing gaps in facility readiness and provider competencies for emergency obstetric care, alongside improving coverage of institutional deliveries, is critical to improve maternal outcomes. It is necessary to strengthen providers' clinical and problem solving skills through capacity building initiatives beyond pre-service training, such as through onsite mentoring and supportive supervision programs. This should be backed by a health systems response to streamline staffing and supply chains in order to improve the quality of emergency obstetric care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".