Assessing the effectiveness of nursing virtual reality simulation for English as second language students to decrease anxiety in clinical courses
Bibliographic record
Abstract
This study aimed to assess the effectiveness of Nursing Virtual Reality Simulation (NVRS) in increasing confidence and reducing anxiety among English as a Second Language (ESL) students enrolled in undergraduate nursing clinical courses. ESL nursing students often face unique challenges in clinical settings, where effective communication and critical thinking are crucial. With the growing use of NVRS as an innovative component of nursing education, this study employed a mixed-methods approach, including a pre-intervention survey and a post-intervention Likert-scale questionnaire, along with individual interviews, to evaluate the outcomes of two NVRS sessions. Preliminary findings suggest that NVRS significantly reduces anxiety related to language barriers, enabling ESL students to practice and improve their communication skills in a supportive, immersive environment. The positive feedback from participants underscores the potential of NVRS to enhance clinical learning experiences. The study concludes that NVRS could be an effective supplementary tool in reducing anxiety among ESL nursing students, and further research is recommended to explore the long-term impacts of NVRS on clinical performance and confidence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".