Measurement and accountability for maternal, newborn and child health: fit for 2030?
Bibliographic record
Abstract
### Summary box As the current global COVID-19 pandemic makes clear, data are power. Now more than ever it is important to reflect on who holds that power and how well it is used to improve global health. This was also on the minds of a group of global health experts at the onset of the Sustainable Development Goals (SDGs). In 2015, after a first meeting in Kirkland, USA, these experts delivered a call to action for a robust maternal, newborn and child health (MNCH) measurement system that could effectively measure and monitor the coverage of high-impact healthcare while also improving capacity to track universal health coverage for women and children.1 That call to action defined five principles. There should be (1) a core focus on a set of indicators; (2) data relevant to countries; (3) measurement innovations; (4) embedded equity analysis and (5) global leadership. Five years later, in 2020, MNCH measurement experts reconvened in Nairobi, Kenya, to reflect on progress against …
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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 | 0.046 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".