Effectiveness of Neonatal Resuscitation Training Programs, Implementation, and Scale-Up in Low- and Middle-Income Countries
Bibliographic record
Abstract
INTRODUCTION: To describe recent evidence regarding the most effective neonatal resuscitation training program and scale-up of these programs in low- and middle-income countries (LMICs), which has contributed to the upcoming Lancet Global Newborn Care Series 2025, and forms part of a supplement describing an extensive synthesis on effective newborn interventions in LMICs. METHODS: We included relevant studies from Medline, Embase, CINAHL, Cochrane CENTRAL and Global Index Medicus databases on the effectiveness and scale-up of Neonatal Resuscitation Training Programs (NRTP), with searches run August 2022. Data extraction and quality assessments were completed independently and in duplicate. RESULTS: A total of 93 unique records met the eligibility criteria and were included in our analyses across the reviews. NRTPs improved most knowledge and skill-based outcomes but impact on mortality varied. Included studies identified knowledge and skill retention, standardized training protocols, and limited training opportunities for health care providers as challenges to current NRTPs. CONCLUSION: Reported knowledge, skills, and mortality outcomes were similar across NRTPs. The Helping Babies Breathe (HBB) program was found to be cost-effective in Tanzania, suggesting that the HBB program or elements thereof are low-cost and scalable in LMICs. Future research across diverse settings should evaluate the cost-effectiveness of other NRTPs. To scale-up current NRTPs, programs should focus on improving long-term retention outcomes and improving training material accessibility. INTRODUCTION: To describe recent evidence regarding the most effective neonatal resuscitation training program and scale-up of these programs in low- and middle-income countries (LMICs), which has contributed to the upcoming Lancet Global Newborn Care Series 2025, and forms part of a supplement describing an extensive synthesis on effective newborn interventions in LMICs. METHODS: We included relevant studies from Medline, Embase, CINAHL, Cochrane CENTRAL and Global Index Medicus databases on the effectiveness and scale-up of Neonatal Resuscitation Training Programs (NRTP), with searches run August 2022. Data extraction and quality assessments were completed independently and in duplicate. RESULTS: A total of 93 unique records met the eligibility criteria and were included in our analyses across the reviews. NRTPs improved most knowledge and skill-based outcomes but impact on mortality varied. Included studies identified knowledge and skill retention, standardized training protocols, and limited training opportunities for health care providers as challenges to current NRTPs. CONCLUSION: Reported knowledge, skills, and mortality outcomes were similar across NRTPs. The Helping Babies Breathe (HBB) program was found to be cost-effective in Tanzania, suggesting that the HBB program or elements thereof are low-cost and scalable in LMICs. Future research across diverse settings should evaluate the cost-effectiveness of other NRTPs. To scale-up current NRTPs, programs should focus on improving long-term retention outcomes and improving training material accessibility.
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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 | 0.038 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".