Helping Babies Survive: Lessons Learned From Global Trainers
Bibliographic record
Abstract
BACKGROUND: The Helping Babies Survive (HBS) suite of programs was launched in 2010 as an evidence-based educational package to train health care workers in low- and middle-income countries in neonatal resuscitation, immediate newborn care, and complications of prematurity. To date, there has been no purposeful examination of lessons learned from HBS trainers. Our intent with this study is to gather that data from the field. METHODS: To estimate the total global reach of the HBS program, we obtained equipment distribution data from Laerdal and HBS material download data from the HBS Web site as of March 2020. To understand the lessons learned from HBS trainers, we examined comments from trainers who recorded their trainings on the HBS Web site, and other first-hand accounts. RESULTS: More than 1 million pieces of equipment (simulators, flip charts, provider guides, and action plans) have been distributed worldwide. HBS materials have been downloaded from the Web site >130 000 times and have now been translated into 27 languages. HBS equipment and training has reached an estimated 850 000 providers in 158 countries. Qualitative analysis revealed 3 major themes critical to building successful and sustainable HBS programs: support, planning and local context, and subthemes for each. CONCLUSIONS: Lessons learned from experienced trainers represent a vital distillation of first-hand experience into widely applicable knowledge to be used to reduce potential failures and achieve desired outcomes. Findings from this study offer further guidance on best practices for implementing and sustaining HBS programs and provide insight into challenges and successes experienced by HBS trainers.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".