Mixed Methods Examination of Challenging and Bothersome Events in Nursing Virtual Simulations: Comparing Screen-Based and Headset VR Modalities
Bibliographic record
Abstract
Background: Virtual simulations (VSs) are increasingly utilized to train nursing students for clinical practice, yet few tools to evaluate VS quality, and little research has examined specific design elements that impact user experience across different modalities. Comparative evidence between screen-based VS and headset virtual reality (VR) within the same VS scenario is especially limited. Purpose: The primary goal of this study is to identify challenging/bothersome events in a VS, compare how these events manifest in screen-based versus headset VR, and examine their impact on electrodermal activity (EDA; physiological arousal), performance, usability, and cognitive load. Methods: A mixed-methods approach was applied to analyze audio-video and EDA data from 20 third-year nursing students during a VS. Objective performance scores were generated by the OMS software, while self-reported data on usability and cognitive load were collected through post-simulation surveys. Results: Six challenging/bothersome event categories were identified: software-related restrictions and bugs, confusion/lack of success, negative affect, technical errors, neglect of instruction, and other. Results revealed that encountering challenging/bothersome events significantly increased students' EDA (an indicator of physiological arousal), possibly reflecting frustration/confusion. Furthermore, while no single challenging/bothersome event category predicted performance or usability relative to others, we found that software-related restrictions and bugs were particularly critical, significantly increasing extrinsic cognitive load compared to technical errors. Conclusions: Our findings highlight specific VS elements that hinder students' experience, directly pointing to areas for future improvements. We also provide valuable insights for educators and developers to enhance virtual learning environments in nursing education by addressing how to reduce cognitive load and improve performance and usability. Future research should develop VS-specific usability tools and replicate this study in diverse contexts to further refine virtual learning experiences.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".