Exploring the Acceptability, Feasibility, and Perceived Effects of Immersive Virtual Reality in Comparison to Standardized Patient Simulations in Nursing Education: A Mixed-Methods Pilot Study
Bibliographic record
Abstract
Purpose: Simulation using immersive virtual reality (IVR) is gaining in popularity in nursing pedagogy. Considering its innovative character, it is essential to tailor the integration of IVR simulation based on the acceptability and feasibility reported by nursing students. Moreover, little is known about its effects compared to other simulation types, such as standardized patient simulation (SPS). This study aimed to compare the acceptability, feasibility, and perceived effects of IVR and SPS activities among undergraduate nursing students. Method: A pilot mixed-methods randomized crossover-controlled trial over two campuses in the province of Quebec was completed. The sample included undergraduate nursing students (n = 14). Participants were randomly assigned to begin with IVR or SPS, followed by the other modality. Data collection included post-assessments regarding acceptability, cognitive load, engagement, situational motivation, and satisfaction after each simulation type. We performed Wilcoxon tests using SPSS. We conducted individual or dyad interviews using a semi-structured interview guide addressing acceptability and feasibility. Three team members analyzed verbatim transcripts. Inductive coding was used to explore emerging ideas, followed by deductive coding to categorize initial codes within the predefined dimensions of acceptability and feasibility from Sidani and Braden’s (2021) framework. Summary tables were produced to condense data. Results: Acceptability was conceptualized in five dimensions: appropriateness, convenience, effectiveness, adherence, and risks. Feasibility was separated into five subthemes: quality of trainers, preparation of participants, material resources, context, and fidelity of the scenario. Participants appreciated the various possibilities and immersive aspects of IVR, such as practising in a safe environment and the innovative, fun experience. A qualitative improvement in patient assessment structure, fluidity, priority establishment, clinical reasoning, and autonomy was also reported. The fidelity of the scenario was deemed higher for IVR than for SPS, according to participants who discussed the use of IVR for evaluation. However, some nuances in implementing IVR, such as targeted competencies, technical problems, equipment comfort, familiarization, and risks of cybersickness should be considered before IVR implementation in nursing education. Furthermore, quantitative results indicated comparable results between IVR and SPS simulations across all variables. No statistically significant difference was found between the two modalities. Conclusion: The implementation of IVR appears acceptable and feasible for undergraduate nursing students, with particular attention to certain factors to ensure optimal outcomes. Quantitative results suggest comparable outcomes, highlighting points of convergence of the two simulation approaches in nursing education. These findings underline the importance of seeking the opinions of primary users when introducing innovative pedagogical interventions. Despite the study’s limitations, this pilot research provides insights into using IVR and SPS activities with nursing students. Future research should focus on testing IVR for evaluation purposes in nursing curricula.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".