Design and implementation of a health systems strengthening approach to improve health and nutrition of pregnant women and newborns in Ethiopia, Kenya, Niger, and Senegal
Bibliographic record
Abstract
Maternal and neonatal mortality are unacceptably high in developing countries. Essential nutrition interventions contribute to reducing this mortality burden, although nutrition is poorly integrated into health systems. Universal health coverage is an essential prerequisite to decreasing mortality indices. However, provision and utilization of nutrition and health services for pregnant women and their newborns are poor and the potential for improvement is limited where health systems are weak. The Community-Based Maternal and Neonatal Health and Nutrition project was established as a set of demonstration projects in 4 countries in Africa with varied health system contexts where there were barriers to safe maternal health care at individual, community and facility levels. We selected project designs based on the need, context, and policies under consideration. A theory driven approach to programme implementation and evaluation was used involving developing of contextual project logic models that linked inputs to address gaps in quality and uptake of antenatal care; essential nutrition actions in antenatal care, delivery, and postnatal care; delivery with skilled and trained birth attendant; and postnatal care to outcomes related to improvements in maternal health service utilization and reduction in maternal and neonatal morbidity and mortality. Routine monitoring and impact evaluations were included in the design. The objective of this paper is to describe the rationale and methods used in setting up a multi-country study that aimed at designing the key maternal and neonatal health interventions and identifying indicators related to inputs, outcomes, and impact that were measured to track change associated with our interventions.
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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 | 0.025 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".