Reduction in End-of-life Care Anxiety in Family Caregivers of Individuals in Palliative Care: Results of a Pilot Exploratory Study Using a Canadian Virtual Reality Technology
Bibliographic record
Abstract
Abstract Virtual reality has demonstrated its efficacy in managing physical and psychological symptoms, but little is known about its role in family caregivers of individuals in palliative care. The virtual reality content «Come with me™» was carefully co-created by the start-up Nipper Media with Canadian stakeholders specialized in palliative care. Codesign resulted in an immersive experience adapted to the context of palliative care. The objective of this study is to explore the effect of Canadian virtual reality content on anxiety in a group of family caregivers of individuals receiving end-of-life palliative care. Participants included 10 family caregivers with an average age of 56.8 years (SD = 22.8, range = 22–78) who tested the virtual reality content «Come with me™» with Pico 3 glasses during a single session of 8.5 min (SD = 0.84) and 22.9 s (SD = 4.25). Outcome measures included the State-Trait-Anxiety-Inventory, the Edmonton Symptom Assessment System Revised, the System Usability Scale, and visual analogue scales of satisfaction and security. After exposure to a single session of virtual reality content, participants reported a significant decrease in anxiety levels without cybersickness symptoms, excellent usability, high levels of satisfaction, and a high perception of security. To our knowledge, this is the first study demonstrating significant positive effects in the reduction of anxiety in a group of family caregivers of individuals in end-of-life care following a short single session of virtual reality content. «Come with me™» is an easy-to-use virtual reality technology for family caregivers in palliative care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".