VR-based simulation training for de-escalation of responsive behaviours in persons with dementia: efficacy and feasibility
Bibliographic record
Abstract
Introduction Caregivers of persons with dementia (PWD) frequently face physical assault and emotional abuse when providing care. Providing experiential opportunities for caregivers to develop skills that maximize safe, compassionate care is a priority. Human simulation has demonstrated greater effectiveness than didactic activities in developing clinical skills. However, this requires consideration of physical safety for both learners and simulated participants (SPs), limiting the full expression of behaviours. To address this limitation, we conducted a proof-of-concept study engaging SPs on a synchronous, facilitated VR platform responding realistically, but safely, to learners’ communication approaches. Learners negotiated online with potential threats of violence from the SPs. Methods This study used a pre/post mixed-method research design. Both qualitative and quantitative approaches were used to explore the impact of this training on participants’ knowledge, confidence and comfort when providing care to PWD. Results Overall, participant ratings of knowledge, confidence and comfort increased post-training, as compared to pre-training (p = 0.28, p = 0.26 and p = 0.70, respectively). Although these increases were not statistically significant, the results were consistent with qualitative data related to these outcomes. However, after adjusting for participants’ prior training in working with PWD, significant increases were associated with the subgroup of novice learners but not for the subgroup who had previous experience (interaction p = 0.004, p = 0.03 and p = 0.02, respectively). Discussion Our findings provide insights into the implications of VR-based training for managing responsive behaviours of PWD. VR training has the ability to increase caregiver knowledge, confidence and comfort working with PWD who are exhibiting responsive behaviours, as shown by participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".