A Simulation-Based Pilot Study of a Mobile Application (NRP Prompt) as a Cognitive Aid for Neonatal Resuscitation Training
Bibliographic record
Abstract
INTRODUCTION: Despite standardized neonatal resuscitation program (NRP) training, retention and adherence to the NRP algorithm remain a challenge. Cognitive aids can potentially improve acquisition and application of NRP knowledge and skills. The objective of this study was to determine whether an interactive mobile application providing audiovisual prompts, NRP Prompt, can help novice NRP providers learn the NRP algorithm more effectively and therefore improve their NRP performance. METHODS: First- and second-year residents from family medicine and obstetrics and gynecology attending NRP training were randomized into intervention and control groups. Resident pairs used standard visual aids with NRP Prompt (intervention) or visual aids only (control) in two simulated neonatal resuscitation training sessions with each resident taking turns as a team leader. Pairs were then evaluated in a third simulation that was video recorded, where neither group used cognitive aids. The primary outcome was comparing resuscitation performance. Secondary outcomes included the following: times to positive-pressure ventilation, intubation, and chest compressions. RESULTS: Thirty-nine residents participated, of which 18 received the intervention. Neonatal resuscitation program performance scores did not significantly differ (P = 0.69). Wilcoxon rank-sum tests showed no significant differences in secondary outcomes of times to positive-pressure ventilation (P = 0.43), intubation (P = 0.44), or chest compressions (P = 0.35). CONCLUSIONS: Training using NRP Prompt did not improve performance scores in simulated neonatal resuscitations immediately after training. Potential reasons include voice prompts in their current format being distracting and lack of customizability to user preferences. Future development of prompting applications should apply a user-centered design approach to optimize the ability to meet end-user needs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".