A Short History of Helping Babies Breathe: Why and How, Then and Now
Bibliographic record
Abstract
Helping Babies Breathe (HBB) changed global education in neonatal resuscitation. Although rooted in the technical and educational expertise underpinning the American Academy of Pediatrics' Neonatal Resuscitation Program, a series of global collaborations and pivotal encounters shaped the program differently. An innovative neonatal simulator, graphic learning materials, and content tailored to address the major causes of neonatal death in low- and middle-income countries empowered providers to take action to help infants in their facilities. Strategic dissemination and implementation through a Global Development Alliance spread the program rapidly, but perhaps the greatest factor in its success was the enthusiasm of participants who experienced the power of being able to improve the outcome of babies. Collaboration continued with frontline users, implementing organizations, researchers, and global health leaders to improve the effectiveness of the program. The second edition of HBB not only incorporated new science but also the accumulated understanding of how to help providers retain and build skills and use quality improvement techniques. Although the implementation of HBB has resulted in significant decreases in fresh stillbirth and early neonatal mortality, the goal of having a skilled and equipped provider at every birth remains to be achieved. Continued collaboration and the leadership of empowered health care providers within their own countries will bring the world closer to this goal.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".