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Record W4386290533 · doi:10.2196/46964

Comparing the Outcomes of Virtual Reality–Based Serious Gaming and Lecture-Based Training for Advanced Life Support Training: Randomized Controlled Trial

2023· article· en· W4386290533 on OpenAlexvenueno aff
Mehmet Emin Aksoy, Arun Ekin Özkan, Dilek Kitapçıoğlu, Tuba Usseli

Bibliographic record

VenueJMIR Serious Games · 2023
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Virtual realityBasic life supportMedical educationTraining (meteorology)Randomized controlled trialComputer scienceTask (project management)MultimediaPsychologyMedicineHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Simulation-based Advanced Cardiac Life Support (ACLS) or Advanced Life Support (ALS) training for health care professionals is important worldwide for saving lives. Virtual reality (VR)-based serious gaming can be an alternative modality to be used as a part of simulation-based ALS training. OBJECTIVE: The aim of this study is to investigate whether a VR-based ALS serious game module can replace classroom-based ALS lectures, the latter being part of existing conventional ALS training protocols in addition to skills training. METHODS: Participants were students from Acibadem Mehmet Ali Aydinlar University's Vocational School for Anesthesiology (N=29) randomly divided into 2 groups with 15 (conventional training group) and 14 (VR-based training group) participants each. Participants in the conventional training group had to complete the pretest consisting of multiple-choice questions at the beginning of the study. Afterward, they took part in an interactive classroom-based ALS lecture. The next step involved skills training with task trainers to teach them compression skills. Following this, the conventional training group was divided into Code Blue teams, each consisting of 5 participants for the simulation session. Two independent instructors evaluated video recordings in terms of technical and nontechnical skills. The score acquired from the manikin-based simulation session was considered the main performance indicator in this study to measure the learning outcome. A similar workflow was used for the VR-based training group, but this group was trained with the VR-based ALS serious game module instead of the theoretical lecture. The final stage of the study involved completing the posttest consisting of multiple-choice questions. A preference survey was conducted among the study participants. Mann-Whitney U and Wilcoxon signed-rank tests were used to analyze the 2 groups' performances in this study. RESULTS: The improvement in posttest results compared with pretest results was significant in the conventional training group (P=.002). Hands-on technical scores of the conventional training group were higher than those of the VR-based training group during manikin-based simulation, but total scores, including those for technical and crisis resource management skills, acquired from the manikin-based simulation session did not reveal any significant difference between the 2 groups. The results of the VR preference survey revealed that the majority of the participants prefer VR-based serious game-based training instead of classroom lectures. CONCLUSIONS: Although hands-on technical scores of the conventional training group during the manikin-based simulation session were higher than those of the VR-based training group, both groups' total performance scores, including those for technical and crisis resource management skills, did not differ significantly. The preference survey reveals that the majority of the participants would prefer a VR-based ALS serious gaming module instead of lecture-based training. Further studies are required to reveal the learning outcome of VR-based ALS serious gaming. TRIAL REGISTRATION: ClinicalTrials.gov NCT05798910; https://clinicaltrials.gov/study/NCT05798910.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
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 head0.080
GPT teacher head0.392
Teacher spread0.312 · 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.

Study designRandomized trial
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
Published2023
Admission routes1
Has abstractyes

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