Lessons Learned From an Evaluation of Serious Gaming as an Alternative to Mannequin-Based Simulation Technology: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The use of new technology like virtual reality, e-learning, and serious gaming can offer novel, more accessible options that have been demonstrated to improve learning outcomes. OBJECTIVE: The aim of this study was to compare the educational effectiveness of serious game-based simulation training to traditional mannequin-based simulation training and to determine the perceptions of physicians and nurses. We used an obstetric use case, namely electronic fetal monitoring interpretation and decision making, for our assessment. METHODS: This study utilized a mixed methods approach to evaluate the effectiveness of the new, serious game-based training method and assess participants' perceptions of the training. Participants were randomized to traditional simulation training in a center with mannequins or serious game training. They then participated in an obstetrical in-situ simulation scenario to assess their learning. Participants also completed a posttraining perceptions questionnaire. RESULTS: The primary outcome measure for this study was the participants' performance in an in-situ mannequin-based simulation scenario, which occurred posttraining following a washout period. No significant statistical differences were detected between the mannequin-based and serious game-based groups in overall performance, although the study was not sufficiently powered to conclude noninferiority. The survey questions were tested for significant differences in participant perceptions of the educational method, but none were found. Qualitative participant feedback revealed important areas for improvement, with a focus on game realism. CONCLUSIONS: The serious game training tool developed has potential utility in providing education to those without access to large simulation centers; however, further validation is needed to demonstrate if this tool is as effective as mannequin-based simulation.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".