CAN A TRAINING MODULE USING VIRTUAL REALITY HELP ADDRESS RESPONSIVE BEHAVIOURS?
Bibliographic record
Abstract
Although there has been an increase in programs addressing responsive behaviours (RB) related to dementia, more is needed as caregivers still face difficulties in real-life situations. Virtual reality (VR) has been shown to give a more lifelike feel to anxiety-provoking training situations by adding psychological realism and an element of stress to interventions. This project aimed to develop and evaluate a VR module that provides a realistic environment in which caregivers, staff and students in health-care fields can gain knowledge and skills on how best to respond to RBs. Existing training materials were surveyed and two RBs were identified for inclusion in the scene: perceived verbal and physical aggression, and perceived resistance to care. The VR scenario is based on three critical moments for interventions from the user in a dining room scene involving interactions with a resident and his granddaughter (both are virtual character models with motion capture of their body and facial expressions). The module is ready to be tested on pre-professional students, staff, professors, and informal carers to determine whether it may be a useful and usable addition to existing training tools in the future. The results of this consultation will be presented as well as a discussion of the relevance for developing a training tool for all those who come in regular contact with individuals with dementia.
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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 | 0.007 |
| 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) | 0.006 | 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".