Achieving Country-Wide Scale for Helping Babies Breathe and Helping Babies Survive
Bibliographic record
Abstract
Helping Babies Breathe (HBB) was piloted in 2009 as a program targeted to reduce neonatal mortality (NM). The program has morphed into a suite of programs termed Helping Babies Survive that includes Essential Care for Every Baby. Since 2010, the HBB and Helping Babies Survive training programs have been taught to >850 000 providers in 80 countries. Initial HBB training is associated with a significant improvement in knowledge and skills. However, at refresher training, there is a knowledge-skill gap evident, with a falloff in skills. Accumulating evidence supports the role for frequent refresher resuscitation training in facilitating skills retention. Beyond skill acquisition, HBB has been associated with a significant reduction in early NM (<24 hours) and fresh stillbirth rates. To evaluate the large-scale impact of the growth of skilled birth attendants, we analyzed NM rates in sub-Saharan Africa (n = 11) and Nepal (as areas of growing HBB implementation). All have revealed a consistent reduction in NM at 28 days between 2009 and 2018; a mean reduction of 5.34%. The number of skilled birth attendants, an indirect measure of HBB sustained rollout, reveals significant correlation with NM, fresh stillbirth, and perinatal mortality rates, highlighting HBB’s success and the need for continued efforts to train frontline providers. A novel live newborn resuscitation trainer as well as a novel app (HBB Prompt) have been developed, increasing knowledge and skills while providing simulation-based repeated practice. Ongoing challenges in sustaining resources (financial and other) for newborn programming emphasize the need for innovative implementation strategies and training tools.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".