Building Self-Efficacy in Dementia Care Through Immersive Education: A Mixed-Methods Randomized Control Trial
Bibliographic record
Abstract
Background Improving self-efficacy for nursing students to manage aggressive behaviours in clients with dementia supports better outcomes for clients. No studies have been conducted on the use of immersive virtual reality as a potential tool. Method A mixed-methods, randomized control trial explanatory design compared perceived self-efficacy for practical nursing students who used the CareGiVRTM virtual reality application with those who did not, using the Inventory of Geriatric Nursing Self-Efficacy (IGNSE) along with qualitative focus groups. Results Forty-six students (49%) participated in the quantitative component. Fifteen students elected to participate in the follow-up qualitative focus groups. Findings indicate participants who used the CareGiVRTM application reported statistically significant higher levels of perceived self-efficacy post-intervention and when compared with those in the control group following their clinical rotation. Four themes were identified: getting real-world experience, a safe place to practice, meeting the client where they are at, and a tool not a replacement. Conclusion Immersive virtual reality can be an effective tool to increase perceived self-efficacy for managing aggressive behaviours in clients with dementia in practical nursing students.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".