Experiences of Patients Undergoing Chemotherapy With Virtual Reality: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Current research into virtual reality (VR) use during chemotherapy shows that it can be an effective distraction intervention. However, there is limited research in adult patients and to investigate how VR can be sustainably implemented in health care organizations. OBJECTIVE: The aim of this study was to explore the feasibility and acceptability of using VR for adult patients undergoing chemotherapy, and to identify the factors that would enable the sustained use of VR during chemotherapy in health care organizations. METHODS: Patients undergoing chemotherapy were recruited to participate in a VR intervention during chemotherapy infusion. Participants were observed during the session and completed a postintervention survey. Each participant was invited to participate in a semistructured interview about their experience. RESULTS: A total of 18 patients participated in the study, 5 of whom participated in semistructured interviews. Findings indicated that the use of VR was acceptable for patients undergoing chemotherapy and the intervention was also feasible. Some participants felt that the VR was an effective distraction during chemotherapy infusion, although most still seemed to be aware of how long their treatment was taking. Although VR was acceptable and feasible to patients, interviews identified several barriers to sustained implementation, including access to a reliable app library and impact on staff workloads. CONCLUSIONS: VR was acceptable to patients with a diagnosis of cancer undergoing chemotherapy treatment. Patients found VR beneficial for breaking up the monotony of treatment, to provide an additional choice of activity in addition to other recreation, and in some instances as a distraction from the treatment itself. However, there are challenges to address if VR is to be implemented in practice for this patient group.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".