Helping Babies Breathe, Second Edition: A Model for Strengthening Educational Programs to Increase Global Newborn Survival
Bibliographic record
Abstract
BACKGROUND: Helping Babies Breathe (HBB), a skills-based program in neonatal resuscitation for birth attendants in resource-limited settings, has been implemented in over 80 countries since 2010. Implementation studies of HBB incorporating low-dose high-frequency practice and quality improvement show substantial reductions in fresh stillbirth and first-day neonatal mortality. Revision of the program aimed to further augment provider and facilitator skills and address gaps in implementation with the goal of improving neonatal survival. METHODS: The Utstein Formula for Survival-Medical Science X Educational Efficiency X Local Implementation = Survival-provided a framework for the revisions. The 2015 Neonatal Resuscitation Consensus on Science and Treatment Recommendations by the International Liaison Committee on Resuscitation informed scientific updates, which were harmonized with the 2012 World Health Organization Basic Newborn Resuscitation Guidelines. Published literature and program reports, consensus guidelines on reprocessing equipment, systematic collection of suggestions from frontline users, and responses to a semistructured online questionnaire informed educational/implementation revisions. Links to maternal care were added. Draft materials underwent Delphi review and field testing in India and Sierra Leone. An Utstein-style meeting of stakeholders identified key actions for successful implementation. RESULTS: Scientific revisions included expectant management of infants with meconium-stained amniotic fluid, limitation of suctioning, and initiating and continuing effective ventilation until spontaneous respirations. Frontline users (N=102) suggested augmented simulation methods to build confidence and competence and additional guidance for facilitators on implementation. Users identified a need for sufficient practice during the workshop, systematized ongoing practice, and enough simulators for participants. Field trials refined approaches to self-reflection, feedback and debriefing, and quality improvement. Utstein meeting stakeholders validated the importance of quality improvement and use of data to improve outcomes. CONCLUSIONS: The second edition of HBB provides a newer paradigm of learning for providers that incorporates workshop practice, self-reflection, and feedback and debriefing to reinforce learning as well as the promotion of mentorship and development of facilitators, systems for low-dose high-frequency practice in facilities, and quality improvement related to neonatal resuscitation.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".