Immersive virtual reality simulation for undergraduate nursing students: Enhancing mental health care for migrants - A mixed method study protocol
Bibliographic record
Abstract
The evolving field of nursing education increasingly integrates innovative methods such as immersive virtual reality (IVR) to improve training outcomes. This protocol paper outlines a study that addresses a significant gap by using IVR to improve mental health care training for undergraduate nursing students, focusing particularly on migrants who frequently encounter access barriers such as stigma, discrimination, and cultural differences. Traditional training methods frequently fail to provide the experiential learning necessary for nursing students to develop deep empathy, cultural competence, cultural humility, and advanced communication skills. A multi-phase, sequential explanatory mixed methods design will be employed in this study, which encompasses three phases: development of IVR simulation, a one-group pre- and post-quasi-experimental design, and an interpretive description approach. Participants will include undergraduate nursing students from the University of Saskatchewan and McGill University. In Phase 1, an integrative review will establish the foundation for the simulation, the findings of which will inform the design of initial simulation drafts on the Unity platform. These drafts will be reviewed by an advisory committee, consisting of migrants experiencing mental health challenges, nursing students, educators, and nurses specialized in migrant health care. Feedback from the committee will be integrated before progressing to Phase 2. Phase 2 involves collecting data through pre- and post-intervention questionnaires completed by participants. This data will be analyzed using descriptive and inferential statistics to assess the impact of the IVR simulation and to inform the next phase of the study. In Phase 3, participants will engage in semi-structured interviews. This phase will employ concurrent data collection and analysis along with constant comparative analysis in an iterative process. Following separate analyses of quantitative and qualitative data, the results will be synthesized to provide a comprehensive interpretation of the findings. The expected outcomes include greater acceptance of IVR as a training tool, positive shifts in student attitudes and behaviours towards migrants with mental health difficulties and enhanced cultural competence. This innovative approach could standardize the use of IVR in nursing curricula, making it a fundamental component of nursing education aimed at preparing students for a diverse and inclusive healthcare environment.
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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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".