End-user involvement in rehabilitation virtual reality implementation research
Bibliographic record
Abstract
PURPOSE: Despite increasing evidence for the effectiveness of off-the-shelf and rehabilitation-specific active video games (AVGs) and virtual reality (VR) systems for rehabilitation, clinical uptake remains poor. A better match between VR/AVG system capabilities and client/therapist needs, through improved end-user involvement (UI) in VR/AVG implementation research, may increase uptake of this technology. The purpose of this paper is to review four case examples from the authors' collective experience of including end users in VR/AVG research to identify common benefits, challenges and lessons learned. DESIGN/METHODOLOGY/APPROACH: The authors apply knowledge and lessons learned from the four cases to make recommendations for subsequent user-engaged research design and methods, including evaluation of the impact of end UI. FINDINGS: A better match between VR/AVG system capabilities and client/therapist needs leads to improved end UI in all stages of VR/AVG implementation research. There are common benefits of increasing buy-in and soliciting early on the knowledge and skills of therapists as well as input from the ultimate end users: people participating in rehabilitation. Most settings have the challenges of balancing the technology requirements with the needs and goals of the practice setting and of the end users. RESEARCH LIMITATIONS/IMPLICATIONS: Increasing end UI in VR/AVG implementation research may address issues related to poor clinical uptake. In the VR/AVG context, end users can be therapists, clients or technology developers/engineers. This paper presented four case scenarios describing the implementation of different VR/AVG systems and involving a variety of populations, end users and settings. ORIGINALITY/VALUE: The set of recommendations for subsequent user-engaged research design and methods span the process of development, research and implementation. The authors hope that these recommendations will foster collaborations across disciplines, encourage researchers and therapists to adopt VR/AVGs more readily, and lead to efficacious and effective treatment approaches for rehabilitation clients.
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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 | 0.132 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".