Immersive Virtual Simulation for Undergraduate Nursing Education on Migrant Mental Health: A Mixed-Methods Study
Bibliographic record
Abstract
Nursing graduates reported feeling unprepared to address migrants' mental health needs. Immersive virtual reality offers an innovative approach to enhance therapeutic communication, cultural competence, and humility. This study examined the acceptability of a virtual reality simulation focused on migrants with mental health challenges and its impact on students' attitudes and cultural competence. A multi-phase sequential mixed methods design was used: phase 1 involved intervention development through an integrative review and a participatory approach; phase 2 employed a one-group pre-quasi-experimental and post-quasi-experimental design; phase 3 employed an interpretive description. Students found the simulation highly acceptable, reporting significant improvements in cultural competence and modest reductions in stigma. Qualitative findings revealed 4 themes: interacting with virtual reality technology; bridging educational gaps; shifting perspectives and practice; and navigating care through lived experiences. Virtual reality shows promise for strengthening mental health nursing education and practice by addressing gaps in clinical placements and traditional teaching. Future research should expand content, improve usability and realism, assess long-term impacts, and support faculty training.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".