Asking different questions: A call to action for research to improve the quality of care for every woman, every child
Bibliographic record
Abstract
Despite decades of considerable economic investment in improving the health of families and newborns world-wide, aspirations for maternal and newborn health have yet to be attained in many regions. The global turn toward recognizing the importance of positive experiences of pregnancy, intrapartum and postnatal care, and care in the first weeks of life, while continuing to work to minimize adverse outcomes, signals a critical change in the maternal and newborn health care conversation and research prioritization. This paper presents "different research questions" drawing on evidence presented in the 2014 Lancet Series on Midwifery and a research prioritization study conducted with the World Health Organization. The results indicated that future research investment in maternal and newborn health should be on "right care," which is quality care that is tailored to individuals, weighs benefits and harms, is person-centered, works across the whole continuum of care, advances equity, and is informed by evidence, including cost-effectiveness. Three inter-related research themes were identified: examination and implementation of models of care that enhance both well-being and safety; investigating and optimizing physiological, psychological, and social processes in pregnancy, childbirth, and the postnatal period; and development and validation of outcome measures that capture short and longer term well-being. New, transformative research approaches should account for the underlying social and political-economic mechanisms that enhance or constrain the well-being of women, newborns, families, and societies. Investment in research capacity and capability building across all settings is critical, but especially in those countries that bear the greatest burden of poor outcomes. We believe this call to action for investment in the three research priorities identified in this paper has the potential to achieve these benefits and to realize the ambitions of Sustainable Development Goal Three of good health and well-being for all.
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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 | 0.427 | 0.470 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.023 | 0.096 |
| Scholarly communication | 0.041 | 0.103 |
| Open science | 0.014 | 0.032 |
| Research integrity | 0.058 | 0.095 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".