Augmented Reality Education Experience (AREduX): An Augmented Reality Experience and Experiential Education Medium to Teach Empathy to Healthcare Providers and Caregivers of Persons Living With Dementia
Bibliographic record
Abstract
Previous research indicates that greater empathy by healthcare providers (HCPs) and informal caregivers leads to better care and improved patient satisfaction and outcomes for persons living with dementia (PLWD). Since few programs exist to train HCPs to develop empathy, we created the augmented reality education experience (AREduX), a proof-of-concept prototype that employs augmented reality (AR) to simulate the physical and cognitive symptoms that PLWD experience. This unique experience simulates the effects of dementia for training purposes with the goal of promoting more empathetic responses from HCPs and informal caregivers when attending to a PLWD. This technical report provides an overview of the five phases of the research program, conceptualization, development and design, usability testing and prototype updating, testing of refined prototype including measuring participants' empathy pre/post interaction with the AREduX, and analysis and dissemination of results, but focuses on Phase 2, development and design. We believe that the AREduX will substantially contribute to the scientific literature on the development of empathy, address the knowledge gap that exists regarding evidence-based understanding of empathy as a construct, and contribute to further recommendations aligned with implementing AR as an experiential education method to enhance empathy among HCPs and caregivers of PLWD.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".