Improved accessibility of emergency obstetrics and newborn care(EmONC) services for maternal and newborn health: a community based project
Bibliographic record
Abstract
BACKGROUND: Every year an estimated three million neonates die globally and two hundred thousand of these deaths occur in Pakistan. Majority of these neonates die in rural areas of underdeveloped countries from preventable causes (infections, complications related to low birth weight and prematurity). Similarly about three hundred thousand mother died in 2010 and Pakistan is among ten countries where sixty percent burden of these deaths is concentrated. Maternal and neonatal mortality remain to be unacceptably high in Pakistan especially in rural areas where more than half of births occur. METHOD/DESIGN: This community based cluster randomized controlled trial will evaluate the impact of an Emergency Obstetric and Newborn Care (EmONC) package in the intervention arm compared to standard of care in control arm. Perinatal and neonatal mortality are primary outcome measure for this trial. The trial will be implemented in 20 clusters (Union councils) of District Rahimyar Khan, Pakistan. The EmONC package consists of provision of maternal and neonatal health pack (clean delivery kit, emollient, chlorhexidine) for safe motherhood and newborn wellbeing and training of community level and facility based health care providers with emphasis on referral of complicated cases to nearest public health facilities and community mobilization. DISCUSSION: Even though there is substantial evidence in support of effectiveness of various health interventions for improving maternal, neonatal and child health. Reduction in perinatal and neonatal mortality remains a big challenge in resource constrained and diverse countries like Pakistan and achieving MDG 4 and 5 appears to be a distant reality. A comprehensive package of community based low cost interventions along the continuum of care tailored according to the socio cultural environment coupled with existing health force capacity building may result in improving the maternal and neonatal outcomes. The findings of this proposed community based trial will provide sufficient evidence on feasibility, acceptability and effectiveness to the policy makers for replicating and scaling up the interventions within the health system.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".