The effects of virtual reality interventions on occupational participation and distress from symptoms in palliative care patients: A pilot study
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) offers the prospect of a safe and effective adjunct therapeutic modality to promote mental health and reduce distress from symptoms in palliative care patients. Common physiological and psychological symptoms experienced at the end of life may impact the person's participation in day-to-day activities that bring them meaning. The purpose of this study was to examine the effect of VR interventions on occupational participation and distress from symptoms. OBJECTIVES: To describe the stimulus, results, and learnings from a single-site pilot study of virtual reality therapy in a specialist palliative care setting. METHODS: Participants engaged in a VR session lasting from 9 to 30 minutes related to coping with pain, inner peace and mindfulness, adventure, and bucket list. METHODS MEASURES: The pilot prospective quantitative observational cohort study was conducted from November 2021 through March 2022 using a pre-post VR intervention research design. Quantitative data was collected using patient-rated assessments and a wireless pulse oximeter. Occupational performance, satisfaction, and distress symptoms were measured using the Canadian Occupational Performance Measure and the Palliative Care Outcomes Collaboration Symptom Assessment Scale (PCOC SAS). The intervention and study design adhered to international guidelines. RESULTS: = .018). No adverse side effects were observed. SIGNIFICANCE OF RESULTS: Outcomes included an analysis of virtual reality's effectiveness to alleviate symptom burden and increase occupational participation for palliative care patients. Of specific interest to the research team was the application of virtual reality in a community-based and inpatient palliative care context to supplement allied health services and its feasibility of integration into standard palliative care. CONCLUSION: VR therapy showed positive improvements in the participants' occupational performance, satisfaction, and distress from pain and fatigue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".