Independent Evaluation of the Reduction of Maternal and Neonatal Mortality in Kenya: Formative Evaluation Findings
Bibliographic record
Abstract
Too many women in Kenya are dying in childbirth. Too many newborn babies don’t survive the first month of their lives. \nThe Government of Kenya is responding with support from international partners. Since 2013, maternity services \nhave been provided free of charge by government hospitals and health centres. However, many challenges remain. There is a strong tradition of home deliveries and hospitals or health centres are often far away. The roads to reach the health centres may not be safe at night, or the fare for the taxi may not be affordable. Throughout the country there is a severe shortage of trained doctors and midwives and many health centres are poorly equipped and may not even have electricity or running water. The DFID-funded Reduction of Maternal and Neonatal Mortality Programme (MNH Programme) started to address these issues in 2014 with a grant of £75.3 million over five years. It is active in six counties, home of nearly one quarter of the Kenya’s population of about 48 million. The midterm evaluation in 2016 found that the MNH Programme addresses some key causes of maternal and newborn health with an appropriate mix of interventions to strengthen the Kenyan health system at all levels, including in the communities. The implementation of some MNH Programme components started late. Training of doctors and midwives in emergency obstetric care was one of the first sets of activities to get underway, and it has started to show results. In 2016, it was, however, still too early for a robust assessment of the number of deaths averted by the programme. Nevertheless, the information collected and documented by the evaluation will serve as a valuable baseline on \nwhich such an assessment can be made in 2018 when the 5-year MNH Programme will be nearing its end.
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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 | 0.118 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".