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Record W4403974870 · doi:10.2196/57057

Impact of a 3-Month Recall Using High-Fidelity Simulation or Screen-Based Simulation on Learning Retention During Neonatal Resuscitation Training for Residents in Anesthesia and Intensive Care: Randomized Controlled Trial

2024· article· en· W4403974870 on OpenAlexvenueno aff
Cécile Dopff, Gauthier Loron, D. Michelet

Bibliographic record

VenueJMIR Serious Games · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialFidelityResuscitationMedicineRecallIntensive careSimulation trainingNeonatal resuscitationAnesthesiaIntensive care medicinePsychologyComputer scienceSimulationSurgery

Abstract

fetched live from OpenAlex

Background: Retention capacities are dependent on the learning context. The optimal interval between two learning sessions to maintain a learner's knowledge is often a subject of discussion, along with the methodology being used. Screen-based simulation could represent an easy alternative for retraining in neonatal resuscitation. Objective: The aim of the study was to evaluate the benefits of a 3-month recall session using high-fidelity simulation or screen-based simulation, assessed 6 months after an initial neonatal resuscitation training session among anesthesia and intensive care residents. Methods: All participating anesthesia and intensive care residents were volunteers, and they underwent training in the same session, which included a theoretical course and high-fidelity simulation. The attendees were then randomized into three groups: one with no 3-month recall, one with a high-fidelity simulation recall, and one with a screen-based simulation recall. To reassess the skills of each participant, a high-fidelity simulation was performed at 6 months. The primary outcomes included expert assessment of technical skills using the Neonatal Resuscitation Performance Evaluation score and nontechnical skills assessed by the Anesthesia Non-Technical Skills score. Secondary outcomes included a knowledge quiz and self-assessment of confidence. We compared the results between groups and analyzed intragroup progressions. Results: Twenty-eight participants were included in the study. No significant differences were observed between groups at the 6-month evaluation. However, we observed a significant improvement in theoretical knowledge and self-confidence among students over time. Regarding nontechnical skills, as evaluated by the Anesthesia Non-Technical Skills score, there was significant improvement between the initial training and the 6-month session in both recall groups (16 vs 12.8, P=.01 in the high-fidelity group; 16 vs 13.9, P=.05 in the simulation group; 14.7 vs 15.1, P=.50 in the control group). For technical skills assessed by the Neonatal Resuscitation Performance Evaluation score, a nonsignificant trend toward improvement was observed in the two recall groups, while a regression was observed in the control group (all Ps>.05). The increase in students' self-confidence was significant across all groups but remained higher in the two 3-month recall groups. Conclusions: Initial neonatal resuscitation training for anesthesia and intensive care residents leads to improved knowledge and self-confidence that persist at 6 months. A 3-month recall session, whether through high-fidelity simulation or screen-based simulation, improves nontechnical skills (eg, situation management and team communication) and technical skills. Screen-based simulation, which saves time and resources, appears to be an effective educational method for recall after initial training. The study outcomes justify the need for further studies with larger sample sizes to confirm the promising role of serious games in educational programs for medical students.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.157
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.412
Teacher spread0.357 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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