Use of Virtual Reality in Interdisciplinary Multimodal Pain Treatment With Insights From Health Care Professionals and Patients: Action Research Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a widespread global health problem. Interdisciplinary multimodal pain treatment (IMPT) is a treatment option for people with chronic pain. Virtual reality (VR) could be used to broaden IMPT programs. However, despite the advantages of VR, it is rarely used in daily clinical practice. OBJECTIVE: This research aimed to explore how, when, and with whom VR can be used meaningfully during IMPT, either as an addition or substitution as a component of IMPT. METHODS: This research used an action research design to help health care professionals and patients learn how, when, and with whom they can use VR. Data were collected through reflection sessions with health care professionals and semistructured interviews with patients in 2 specialized centers that provide IMPT for chronic pain. Two researchers performed direct content analyses. RESULTS: In total, 4 physiotherapists, 1 occupational therapist, 3 psychologists, and 22 patients participated in this research. Three iteration cycles, including 9 reflection sessions and 8 semistructured interviews, were performed. Both health care professionals and patients considered VR to be useful in therapy as an addition but not a substitution. VR was used as a diagnostic and intervention tool with all patients at the rehabilitation center or home. VR was used to gain insight into patients' pain beliefs, cognitions, and irrational cognitions about additional damage and physical abilities. Considering VR as an intervention tool, the health care professionals had 3 goals: balancing relaxation and competition, grading activities, and exposure in vivo. CONCLUSIONS: VR could be a valuable addition to IMPT for both patients with chronic pain and health care professionals. More research should be performed to assess the additional effects of VR on patients' participation in daily life.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".