Impact of Be EPIC‐VR training on person‐centered communication in dementia care
Bibliographic record
Abstract
Abstract Background Be EPIC‐VR is a novel person‐centered communication training program for frontline healthcare workers who support persons living with dementia. The aim of this study was to investigate the effectiveness of Be EPIC‐VR on increasing frontline staff's use of person‐centered communication (PCC) during care interactions. Method Personal support workers were assigned to either the immediate training group ( N = 26) or the wait‐list control group ( N = 18). Participants engaged in a 10‐minute video‐recorded interaction with a virtual avatar depicting a person living with dementia pre‐ and post‐training. Conversation data was transcribed into communication units, coded if indicated and analyzed by frequency of occurrence for the use of PCC. Outcome measures were a) the proportions of Be EPIC‐VR's PCC units (coded as recognition, negotiation, facilitation, validation) and b) the proportions of missed opportunities for PCC (coded as omissions or alternatives). Result A 2 (Group: immediate training vs. control) by 2 (Time: pre vs. post) mixed ANOVA on the proportion of PCC units revealed a significant group by time interaction ( F (1, 42) = 12.86, p < .001, η p 2 = .24). Simple effects analyses showed that the immediate training group exhibited a significant increase in PCC from pre‐ to post‐training, whereas there was no change for the control group. Similarly, the ANOVA for missed opportunity revealed a significant group by time interaction ( F (1, 42) = 17.28, p < .001, η p 2 = .29). Follow‐up simple analyses showed that the immediate training group exhibited a significant decrease in missed opportunities for PCC from pre‐ to post‐training, whereas the control group had no change. Additional one‐way repeated measures ANOVA analysis combined participants who completed Be EPIC‐VR (immediate training and waitlist group once they completed training). There were significant increases in PCC ( F (1,43)=34.19, p < .001, η p 2 =.44) and significant decreases in missed opportunities ( F (1,43)=34.59, p < .001, η p 2 =.45) from pre‐ to post‐Be EPIC‐VR training in this combined group. Conclusion Findings show that Be EPIC‐VR enhanced person‐centered communication, which is essential for optimizing quality of care. The findings highlight the importance of performance‐based outcomes when assessing person‐centered care interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".