Devolving countdown to countries: using global resources to support regional and national action
Bibliographic record
Abstract
Background As the world embarks on the quest to achieve the sustainable development goals (SDGs), building on the momentum and lessons of the millennium development goals (MDGs), several aspects are clear. The SDGs are deliberately visionary and all-encompassing and in relation to health and nutrition, and include most of the social determinants of health. The health goal is also a much broader goal than the focus on maternal and child health and infectious diseases that was found in the MDGs. Notwithstanding the above, three key aspects of the health goal (SDG 3) related to maternal and child health stand out. Firstly, achieving further gains in maternal and child health and survival cannot depend on the momentum of the past decade and will need concerted action and a focus on the bottlenecks and disparities highlighted previously [1]. Secondly, the renewed global strategy for every woman every child, The Global Strategy for Women’s, Children’s and Adolescents’ Health (Global Strategy), now includes several aspects of the continuum of care for women and children that were hitherto ignored. These include aspects of adolescent health, preconception care as well as child development outcomes. Lastly the focus on social determinants of health in the SDGs opens up huge opportunities for investments, multi-sectoral action and accountability. The transition from the successful Countdown to 2015 (Countdown 2015) activities to Countdown 2030
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 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".