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Record W2938333356 · doi:10.1097/sih.0000000000000353

A Simulation-Based Pilot Study of a Mobile Application (NRP Prompt) as a Cognitive Aid for Neonatal Resuscitation Training

2019· article· en· W2938333356 on OpenAlexaff
Natalie Chan, Niraj Mistry, Douglas M. Campbell

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2019
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsNeonatal resuscitationMedicineIntubationIntervention (counseling)ResuscitationCognitionPsychological interventionPhysical therapyEmergency medicineNursingAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.076
GPT teacher head0.425
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2019
Admission routes1
Has abstractyes

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