Remote Facilitation of Essential Newborn Care: A Multinational, Multicenter Pilot Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES Essential Newborn Care (ENC) training improves neonatal outcomes, particularly in low-resource settings. The American Academy of Pediatrics and Laerdal Global Health developed an online platform, https://hmbs.org/, to facilitate training of health care workers (HCWs) in ENC. This study aims to examine the impact of training with the ENC course. We hypothesize that training with the ENC course, either with remote or in-person facilitation, and low-dose high-frequency (LDHF) practice improves HCW knowledge and skills. METHODS In this prospective educational intervention study, technical advisors remotely oriented in-country facilitators to ENC in Nigeria and Bangladesh. In-country facilitators then trained frontline HCWs, choosing between a remote or in-person approach based on the country context. ENC knowledge check, bag-mask ventilation (BMV) skills, and NeoNatalie Live (NNL) manikin feedback pass rates were assessed at baseline (BL), immediately posttraining (PT), and endline (EL). Objective structured clinical examination (OSCE) A and B scores were assessed at PT and EL. LDHF practice was implemented at all sites using the NNL manikin. RESULTS After remote orientation, 18 in-country facilitators trained 236 frontline HCWs at 8 sites, including 1 humanitarian setting. All sites showed significant improvement in pass rates from BL to PT in knowledge check and BMV skills. NNL pass rates generally improved from PT to EL. OSCE A and B pass rates were also generally high PT and maintained at EL. CONCLUSIONS ENC online materials coupled with LDHF practice can augment knowledge and skills in ENC and offer a flexible option for remote or in-person training.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".