Measurement and accountability for maternal, newborn and child health: fit for 2030?
Bibliographic record
Abstract
► At the onset of the Sustainable Development Goals, in 2015, a group of global health experts delivered a call to action for an improved measurement system for women's and children's health.► Five principles were defined, including having a focused set of core indicators, making data relevant to countries, investing in innovations, embedding equity measures and supportive global leadership.► Five years later, in 2020, a second meeting reviewed progress against these principles and identified gaps and opportunities for investment in the coming decade.► The greatest opportunity now is to make an intentional shift from global to local actions that strengthen measurement systems in the locations where they are needed.► Greater country ownership of the measurement and accountability agenda is needed to promote more context-specific actions that reflect multisectoral realities, and that are supported by a responsive and adaptable measurement community.
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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 | 0.167 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.027 | 0.055 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.024 | 0.049 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".