Anticipated Barriers and Facilitators to Engaging in a Novel Virtual Reality Program for Lower Phantom Limb Pain
Bibliographic record
Abstract
While there is promising evidence to suggest certain virtual reality (VR) programs and graded motor imagery (GMI) can independently be administered to treat phantom limb pain (PLP) in people with lower limb amputations (LLAs), there are many barriers preventing their implementation. Long outpatient wait times prevent treatment access in the early postoperative period following amputation, when PLP is the most severe. The integration of GMI in VR offers the opportunity to improve PLP treatment access in the acute period by facilitating self-administration. Accordingly, the present multidisciplinary team used a multi-methods approach to assess a novel head-mounted VR GMI prototype and evaluate areas of improvement according to patient feedback. Twelve people with unilateral LLAs recruited from outpatient physiotherapist and prosthetic clinics were asked to trial the program in a single intensive session. Afterwards, participants completed a semi-structured interview to reflect on barriers and facilitators they expect would affect their use of the VR GMI program. They also completed psychometrically validated self-report questionnaires (including the User Engagement Scale and Presence Questionnaire) that inquired about the engagement and immersion facilitated by the program. Reflexive thematic analysis suggests VR and treatment expectations, in addition to individual priorities, motivation, and resources, may affect one's willingness to use the VR GMI program in the acute period following amputation. Meanwhile, descriptive analysis demonstrates that while the VR GMI program is considered immersive, more focused attention needs to be facilitated to increase engagement. In line with these findings, future development will prioritize embedding psychoeducation to prime realistic expectations about the intervention. These developments will improve the chances of implementing the VR GMI program in clinical settings following a larger study to assess its efficacy in the hospital following amputation. Results may be transferred to our broader understanding of how VR interventions may be implemented in acute postoperative settings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".