Exploring User Needs in the Development of a Virtual Reality–Based Advanced Life Support Training Platform: Exploratory Usability Study
Bibliographic record
Abstract
BACKGROUND: Traditional methods of delivering Advanced Life Support (ALS) training and reaccreditation are resource-intensive and costly. Interactive simulations and gameplay using virtual reality (VR) technology can complement traditional training processes as a cost-effective, engaging, and flexible training tool. OBJECTIVE: This exploratory study aimed to determine the specific user needs of clinicians engaging with a new interactive VR ALS simulation (ALS-SimVR) application to inform the ongoing development of such training platforms. METHODS: Semistructured interviews were conducted with experienced clinicians (n=10, median age=40.9 years) following a single playthrough of the application. All clinicians have been directly involved in the delivery of ALS training in both clinical and educational settings (median years of ALS experience=12.4; all had minimal or no VR experience). Interviews were supplemented with an assessment of usability (using heuristic evaluation) and presence. RESULTS: The ALS-SimVR training app was well received. Thematic analysis of the interviews revealed five main areas of user needs that can inform future design efforts for creating engaging VR training apps: affordances, agency, diverse input modalities, mental models, and advanced roles. CONCLUSIONS: This study was conducted to identify the needs of clinicians engaging with ALS-SimVR. However, our findings revealed broader design considerations that will be crucial in guiding future work in this area. Although aligning the training scenarios with accepted teaching algorithms is important, our findings reveal that improving user experience and engagement requires careful attention to technology-specific issues such as input modalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".