Comparing the Outcomes of Virtual Reality–Based Serious Gaming and Lecture-Based Training for Advanced Life Support Training: Randomized Controlled Trial
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".