User Experience Testing of the Meta Quest 2 for Integration With the Virtual Reality Simulation for Dementia Coaching, Advocacy, Respite, Education, Relationship, and Simulation (VR-SIM CARERS) Program
Bibliographic record
Abstract
Caregivers (CGs) of persons with dementia (PWDs) face numerous challenges, including learning about the condition, managing behavioral symptoms, and prioritizing their own well-being. Virtual reality (VR) technology has emerged as a promising tool to adopt certain elements of existing CG psychoeducation programs, such as the Reitman Centre CARERS (coaching, advocacy, respite, education, relationship, and simulation) program, which has been shown effective in reducing CG burden and stress and building the required skills for caring for PWD. Recently, we have developed a VR prototype utilizing Meta Quest 2 (Meta, Menlo Park, CA, USA), which will be referred to as the (virtual reality simulation for dementia CARERS) VR-SIM CARERS program. This technical report aims to describe the early stages of intervention modeling by testing user experiences related to the hardware used. The Meta Quest 2 VR system is chosen for its accessibility and functionality, aiming to ensure widespread access. Through interviews and observational techniques, we explored CGs age-matched controls' attitudes, comfort, and proficiency with the Meta Quest 2 VR system, which are crucial for informing technological choices. Initial findings revealed mixed attitudes, comfort, and proficiency about the Meta Quest 2 VR system. Although further testing of the Meta Quest 2 VR system within the CG community is warranted, the interpretation of these preliminary results indicates that the VR-SIM CARERS program should have minimal technological skill requirements for user engagement or provide in-depth training resources for the CGs who choose to use the system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".