Integrating Branching Spherical Video Learning into Mental Health Nursing Clinical Education: Feasibility, Efficacy, and Student Impact
Bibliographic record
Abstract
Background Nursing education faces significant challenges in providing students with adequate clinical learning experiences, particularly in mental health. Anxiety among nursing students related to clinical practice is well-documented and can hinder effective learning and performance. Methods This pilot study aimed to assess the feasibility and effectiveness of using Branching spherical video learning scenarios to reduce student anxiety and enhance mental health assessment knowledge in undergraduate nursing students. A mixed-methods approach, including quasi-experimental design and qualitative interviews, was employed. Participants were randomly assigned to intervention and control groups, with the intervention group experiencing the learning scenario during their clinical course. Results Quantitative analysis revealed reductions in anxiety and increases in confidence among the intervention group postintervention. Qualitative interviews confirmed reduced anxiety, increased confidence, and enhanced mental status examination (MSE) knowledge among participants. Conclusion Branching spherical video learning scenarios show promise in alleviating student anxiety and improving mental health assessment knowledge in nursing education. The study underscores the potential of immersive VR technologies to enhance learning experiences and prepare students for clinical practice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".