The usability of virtual reality to train individuals in responding to behaviors related to dementia
Bibliographic record
Abstract
Introduction: (BPSD) of which many are recognized to be the expression of unmet needs triggered by environmental factors. BPSD are an extreme source of stress for family care partners and health care providers alike and can be the reason why people living with dementia (PLWD) are placed in long-term care homes (LTCH). The overall goal of this project was to examine whether a virtual environment that includes a virtual LTCH resident with dementia in a lifelike situation could be useful and usable for health care providers and care partners to identify potential triggers to BPSDs while being engaged emotionally with the scenario. Methods: Twenty-three health care professionals working with PLWD, 25 care partners to PLWD, 27 students in a health-related field, and 11 university/community college faculty members teaching courses relevant to gerontology tested the application which depicted a meal-time scenario. In addition to being asked about the behavioral triggers in the scene, participants were asked about the usefulness and usability of the tool for training. Presence and simulator sickness were also measured. Results: Results suggest that participants generally felt present and emotionally engaged. They could identify the potential triggers for the observed behaviors in the virtual human with dementia as well as suggest some solutions. The majority (87% of participants) found the tool easy to use. Many participants identified the inability to interact with the virtual humans as a shortfall, and few reported mild to moderate levels of simulator sickness. Discussion: As the behavioral changes associated with dementia can cause extreme stress for those interacting with PLWD, developing an effective and efficient training tool could significantly improve well-being for all involved. The investigators see the development and testing of an interactive version of this virtual environment as a next step in making this a clinically relevant training tool.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".