Asking different questions: research priorities to improve the quality of care for every woman, every child
Bibliographic record
Abstract
Unacceptably high rates of adverse outcomes persist for childbearing women and infants, including maternal and newborn mortality, stillbirth, and short-term and long-term morbidity.1 In light of the challenges to achieve the UN Sustainable Development Goals, it is timely to reconsider priorities for research in maternal and newborn health. Are we asking the right questions?2 Recent evidence indicates the importance of seeking knowledge beyond the treatment of complications, to inform better ways of providing sustainable, high quality care, including preventing problems before they occur.
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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 | 0.235 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.031 | 0.046 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.030 | 0.053 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".