Supporting virtual reality and active video game use in pediatric rehabilitation: Protocol for a mixed-methods feasibility study
Bibliographic record
Abstract
Abstract Background Virtual reality (VR) and active video game (AVG) systems that offer repetitive practice and multimodal feedback in engaging environments are attractive pediatric rehabilitation intervention options. Evidence supports their effectiveness to improve functional outcomes in multiple pediatric populations. However, VR/AVG integration into clinical practice faces multiple barriers, including limited access to these often-expensive technologies, a rapid development sector resulting in frequent obsolescence, and insufficient educational resources to help clinicians select appropriate games that match children’s therapeutic objectives. Knowledge translation initiatives that primarily target clinician knowledge and attitudes about VR/AVG use have shown limited success in facilitating adoption. To address these challenges, we used the Consolidated Framework of Implementation Research (CFIR) to structure development of the Technotheque, a multi-faceted VR/AVG support initiative at our large pediatric rehabilitation centre. The Technotheque addresses both access- and knowledge-based barriers to VR/AVG use via a dedicated gameplay space staffed by knowledge brokers who directly support clinicians in VR/AVG implementation in therapy sessions and provide education towards independent VR/AVG use. Objective To evaluate the feasibility of the Technotheque as a knowledge translation initiative to enhance VR/AVG use at our pediatric rehabilitation centre, as measured by demand, acceptability, adaptation, and implementation criteria. Methods Convergent mixed-methods design. We will use convenience and snowball sampling strategies to recruit clinicians (physiotherapists, occupational therapists, speech therapists, special educators, and neuropsychologists) to participate in this 4-month study. Following informed consent, participants will complete a modified ‘Assessing the Determinants of Prospective Take-up of Virtual Reality’ (ADOPT-VR2) instrument. Participants can then request individualized training and/or clinical implementation support with our knowledge brokers at a self-determined frequency and duration, and with their choice of clientele. Participants will be free to begin and end their study participation at any point during the four months. Data collection will include study-specific Technotheque pre-session objective and post-session feedback forms, a post-study ADOPT-VR reassessment, a CFIR-based satisfaction questionnaire, and individual semi-structured interviews. Quantitative analyses will examine demand (participant demographics, usage patterns, correlations between usage and ADOPT-VR2 scores), acceptability (satisfaction scores in relation to usage patterns and ADOPT-VR2 changes), adaptation (variety of professions, client populations, and clinical objectives), and implementation (pre-post ADOPT-VR2 changes and patterns of support needs over time). Qualitative data will be deductively analysed using the four feasibility criteria as a coding framework. Quantitative and qualitative results will be integrated to identify areas of alignment and/or divergence. Results Research Ethics Board approval has been obtained. Conclusions The Technotheque initiative is designed to address both access and knowledge barriers to VR/AVG use in pediatric rehabilitation. Study results will inform subsequent research efforts to evaluate the effectiveness of VR/AVG as a rehabilitation intervention and to examine the impact of this initiative on sustained VR/AVG use.
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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 | 0.073 | 0.055 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".