Virtual Neonatal Resuscitation Curriculum for Emergency Medical Services (EMS) to Improve Out-of-Hospital Newborn Care
Bibliographic record
Abstract
Objectives Out-of-hospital births are associated with a 2- to 11-fold increased risk of death compared to in-hospital births and are growing. Emergency Medical Services (EMS) clinicians have limited exposure to hospital birth emergencies, and there is no standardized prehospital neonatal resuscitation curriculum. Neonatal Resuscitation Program (NRP) guidelines are the standard of care for infants born in the United States but focuses on in-hospital births and is not easily applied to EMS. There is a need for tailored NRP training to meet EMS clinicians’ specific needs, context, and systems.Methods This was a prospective observational study of a virtual EMS-tailored, newborn resuscitation curriculum focused on initial steps of newborn resuscitation in the out-of-hospital setting. The initial content (90-minute) was pilot tested virtually among 350 urban EMS clinicians, with favorable feedback (89% survey response rate). Based on feedback, we created a 60-minute interactive, virtual curriculum that includes NRP-based didactic and memory aids to reinforce how NRP differs from pediatric resuscitation designed specifically for EMS. The course also includes video demonstrations with pauses for hands-on self-directed skills practice. We delivered the curriculum to clinicians from 17 EMS agencies in rural Oregon. To assess neonatal resuscitation knowledge acquisition and retention, participants completed the same 10-question test before, after, and 3 months following the training. Questions were adapted from the 8th Edition NRP Textbook and NRP test questions.Results Eighty-four EMS clinicians completed the pretest, curriculum, and post-test and demonstrated improvement in immediate post-curriculum NRP knowledge (pretest mean score 5.32 ± 1.99; post-test mean score 8.61 ± 1.26; p < 0.001). Forty participants completed the 3-month follow up test and scores remained improved from baseline (3 month-follow up mean score 6.88 ± 1.83, p < 0.001). Prehospital clinicians (N = 84) thought that this EMS-tailored NRP curriculum was easy to complete (100%), valuable to their clinical practice (99%), and filled a gap in their education (98%). They felt that implementing/requiring this training is possible/doable (99%) and recommend the curriculum to other EMS agencies (99%).Conclusions A virtual EMS-tailored, NRP-based educational curriculum improved neonatal resuscitation knowledge immediately and was sustained at 3 months compared to baseline. The curriculum is feasible and acceptable to EMS clinicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".