Patients perceptions of virtual reality therapy in the management of chronic cancer pain
Bibliographic record
Abstract
The management of chronic cancer pain remains challenging and complex, with the process often involving a variety of pharmacological and non-pharmacological approaches. Recent studies have shown virtual reality (VR) therapy to be successful in the management of acute pain. However, it remains unclear whether VR-based applications are effective as an adjunctive therapy for cancer patients with chronic pain. Moreover, there exists a gap in the current research landscape that address patient's perceptions of virtual reality therapy. This qualitative study enrolled patients from a larger ongoing randomized controlled clinical trial in two focus groups covering topics including patients experience with and perspectives on using VR for chronic pain control, both generally, and specific to their own circumstances. Five major thematic categories and 23 sub-categories emerged in the analysis process reflecting the participants' narrative. Similar to other research, this study found mixed results in the use of adjunctive VR therapy to manage chronic cancer pain, although a majority of respondents found it to be beneficial. This study confirms that pain management is a highly complex and individualized process. For maximum efficacy, it is recommended that future designs of VR interventions engage pain patients in the design process to ensure maximum efficacy of experiences to with individuals' preferences.
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 | 0.004 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".