149 The potential for virtual reality therapy in palliative care – preliminary findings
Bibliographic record
Abstract
Background Researchers have long been interested in the physiological and psychological aspects of wellbeing. Studies have found that Virtual Reality (VR) therapy, using computer generated environments and avatars, can have positive effects in relieving pain in some patient populations and can be used to alleviate symptoms of depression, anxiety, and Post Traumatic Stress Disorder. To date, there is little published research about the physical and psychological impact of VR therapy using real life environments/settings and the potential for VR to be used effectively in palliative care has not been fully investigated. Method Participants are given a single VR therapy experience lasting no more than 4 min, once a week, for 4 weeks. Quantitative data is obtained through the comparative analysis of pre- and post-session Edmonton Symptom Assessment System: Revised (ESAS-R) scores and qualitative data is gathered through set interviews completed after each VR therapy. Results Preliminary data evidences a 53% mean reduction in pain symptoms, 66% mean reduction in fatigue, 60% mean reduction in drowsiness, 50% mean reduction of SOB, 52% mean reduction in depression, and 62% mean reduction in anxiety with an overall 49% mean increase in overall wellbeing. Qualitatively, participants spoke about feeling more relaxed and generally ‘happier’ as a result of VR. Additionally participants spoke about VR connecting them to positive memories as well giving them a sense of freedom both from their illness, their symptoms, and life as a patient. Conclusion Preliminary findings positively demonstrate a reduction of common physiological and psychological palliative care symptoms. Additional participants and VR therapy sessions are planned. Additional positive results will provide robust evidence for VR Therapy to be adopted and used alongside current symptom control measures used 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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".