“Kinect-ing” With Clinicians: A Knowledge Translation Resource to Support Decision Making About Video Game Use in Rehabilitation
Bibliographic record
Abstract
Microsoft's Kinect for Xbox 360 virtual reality (VR) video games are promising rehabilitation options because they involve motivating, full-body movement practice. However, these games were designed for recreational use, which creates challenges for clinical implementation. Busy clinicians require decision-making support to inform game selection and implementation that address individual therapeutic goals. This article describes the development and preliminary evaluation of a knowledge translation (KT) resource to support clinical decision making about selection and use of Kinect games in physical therapy. The knowledge-to-action framework guided the development of the Kinecting With Clinicians (KWiC) resource. Five physical therapists with VR and video game expertise analyzed the Kinect Adventure games. A consensus-building method was used to arrive at categories to organize clinically relevant attributes guiding game selection and game play. The process and results of an exploratory usability evaluation of the KWiC resource by clinicians through interviews and focus groups at 4 clinical sites is described. Subsequent steps in the evaluation and KT process are proposed, including making the KWiC resource Web-based and evaluating the utility of the online resource in clinical practice.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".