The National Evaluation Platform for Maternal, Newborn, and Child Health, and Nutrition: From idea to implementation
Bibliographic record
Abstract
A ccelerating progress in women' s and children' s health requires scaling up efficacious interventions and measuring progress towards defined targets. However, determining what is effective in a particular setting and optimizing investments is challenging given the complexity of health systems and the diversity of contexts surrounding maternal, newborn, and child health and nutrition (MNCH&N) policies and programs in low-and middle-income countries (LMICs). There have been various global efforts to synthesize evidence (eg, World Health Organization Guidelines; various Lancet series on maternal child health and nutrition issues, Cochrane Collaborative reviews, Disease Control Priorities Project and monitor progress towards shared goals (eg, Sustainable Development Goals, World Health Assembly 2025 Nutrition Targets, the Countdown to 2030, Family Planning 2020) which have some influence on country-level priorities and plans [1-6]. Ultimately, however, national and sub-national stakeholders want evidence from their country to guide their policy and program decisions. Too often this evidence is not available when and where decisions makers need it.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".