Advancing measurement and monitoring of reproductive, maternal, newborn and child health and nutrition: global and country perspectives
Bibliographic record
Abstract
Aligned with the Sustainable Development Goals, the Global Strategy for Women’s, Children’s and Adolescents’ Health (2016–2030) represents an essential shift in prioritisation for actions designed to help families live healthy, secure lives and fulfil their economic potential.1 The reproductive, maternal, newborn, child and adolescent health and nutrition (RMNCAH-N) agenda is now both broader and more complex than was the case during the Millennium Development Goal era, creating a need for new data. To contribute to this need, Countdown to 2030 for Women’s, Children’s and Adolescents’ Health (Countdown), a multi-institutional network of academics from institutions around the world and representatives from United Nations agencies and civil society, aims to enhance monitoring and measurement of women’s, children’s and adolescents’ health globally and in countries.2 In 2018, Countdown organised a measurement conference in Stellenbosch, South Africa, that brought together 100 experts in multiple areas of RMNCAH-N, which resulted in the six papers in this supplement and an overall research agenda. The manuscripts in this collection represent the first developments of Countdown’s work to enhance measurement. They identify some of the persistent measurement and monitoring gaps in RMNCAH-N, for example, by reviewing the evidence on methods for generating effective coverage estimates and presenting actionable analytical methods to identify inequalities within and between countries. The collection also considers measurement advances for early childhood development and for nutrition. Further, it expands to analyse new priority issues, including using national surveys to analyse the impact of armed conflicts on RMNCAH-N;3 and describing the new data needed to better understand the social, political and contextual complexity of health system governance. Countdown will continue to extend this measurement improvement agenda. In some aspects, however, the measurement and monitoring of RMNCAH-N is more advanced than other health areas, such as infectious diseases, non-communicable diseases, injuries and mental …
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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 | 0.123 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".