Improving global maternal and newborn survival via innovation: Stakeholder perspectives on the Saving Lives at Birth Grand Challenge
Bibliographic record
Abstract
The Saving Lives at Birth (SL@B) funding partners joined in 2011 to source, support, and scale maternal and newborn health (MNH) innovations to improve maternal and newborn survival by focusing on the 24 hours around the time of birth. A multi-methods, retrospective portfolio evaluation was conducted to determine SL@B's impact. Forty semi-structured, key informant interviews (KIIs) were conducted with experts in global MNH based in low- and middle-income and in high-income countries to assess the SL@B program. KIIs were conducted with global MNH technical experts, innovators, government officials in low- and middle-income countries, donors, private investors, and implementing partners to include the full spectrum of voices involved in identifying and scaling innovations. Data were analyzed using thematic analysis. Stakeholders believe the SL@B program has been successful in changing the way maternal and newborn health programs are delivered with a focus on doing things differently through innovation. The open approach to sourcing innovation was seen as positive to the extent that it brought more interdisciplinary stakeholders to think about the problem of maternal and newborn survival. However, a demand-driven approach that aims to source innovations that address MNH priority needs and takes into account the needs of end users (e.g. individuals and governments) was suggested as a strategy for ensuring that more innovations go to scale.
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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 | 0.035 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".