The impact of the Helping Babies Survive program on neonatal outcomes and health provider skills: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The objective of this review was to evaluate the impact of the Helping Babies Survive program on neonatal outcomes and healthcare provider knowledge and skills. INTRODUCTION: The Helping Babies Survive program consists of three modules: Helping Babies Breathe, Essential Care for Every Baby, and Essential Care for Small Babies. It was developed to reduce preventable newborn deaths through skill-based learning using simulation, learning exercises, and peer-to-peer training of healthcare providers in low-resource areas. Despite the widespread increase in healthcare provider training through Helping Babies Survive and the growing number of studies that have been conducted, there has been no systematic review of the Helping Babies Survive program to date. INCLUSION CRITERIA: The review included studies on healthcare providers and/or birth attendants providing essential neonatal care during and post birth. Types of interventions were any Helping Babies Survive module (Helping Babies Breathe, Essential Care for Every Baby, Essential Care for Small Babies). Studies including experimental study designs with the following outcomes were considered: neonatal outcomes and/or healthcare provider knowledge and skills obtained, maintained, and used over time. METHODS: PubMed, Embase, Web of Science, ProQuest Databases, Scopus and CINAHL were searched for published studies in English between January 2010 to December 2016. Critical appraisal was undertaken by two independent reviewers using standardized critical appraisal instruments from the Joanna Briggs Institute (JBI). Conflicts were solved through consensus with a third reviewer. Quantitative data were extracted from included studies independently by two reviewers using the standardized data extraction tool from JBI. Conflicts were solved through consensus with a third reviewer. Quantitative data was, where possible, pooled in statistical meta-analysis using RevMan (Copenhagen: The Nordic Cochrane Centre, Cochrane). Where statistical pooling was not possible the findings have been reported narratively. RESULTS: A total of 17 studies were identified - 15 on Helping Babies Breathe (n = 172,685 infants and n = 2,261 healthcare providers) and two on Essential Care for Every Baby (n = 206 healthcare providers). No studies reported on Essential Care for Small Babies. Helping Babies Survive was found to significantly reduce fresh stillbirth rates and first day mortality rates, but was not found to influence stillbirth rates or mortality rates, measured at seven or 28 days post birth. Short-term improvements were significant in knowledge and skills scores but not significant in sustainability over time. Additionally, implementation of resuscitations skills in clinical practice related to the Helping Babies Breathe module including drying/stimulation, suction, and bag and mask ventilation did not show a significant increase after training even though the number of fresh stillbirth and first-day mortality rate decreased. CONCLUSIONS: Helping Babies Survive has a significant positive impact on early neonatal outcomes, including fresh stillbirth and first-day mortality primarily through Helping Babies Breathe, but limited conclusions can be drawn about its impact on other neonatal outcomes. While Helping Babies Survive was found to improve immediate knowledge and skill acquisition, there is some evidence that one-time training may not be sufficient for sustained knowledge or the incorporation of key skills related to resuscitation into clinical practice. Continued research on the sustained knowledge and skills is needed to evaluate the long-term impact of the Helping Babies Survive program.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".