Commercial gaming for fine motor recovery with survivors of chronic stroke: Participants' evaluations and recommendations
Bibliographic record
Abstract
Background: The use of commercial gaming and virtual reality for stroke rehabilitation has increased substantially. However, very few studies have included the participants’ evaluations and recommendations for survivors of chronic stroke when using these rehabilitation techniques. Examining the experiences that participants have towards commercial gaming and virtual reality are important in determining the sustainability of these tools, and for the future development of techniques that participants will enjoy. Purpose: To examine the experiences of chronic stroke participants who interacted with an off-the-shelf commercial gaming device in a community-level rehabilitation setting. Methods: Individual, semi-structured interviews were completed with ten participants in the chronic phase of stroke recovery following their involvement with a commercial gaming intervention designed to improve hand function. Interviews were transcribed verbatim and reviewed line by line for rich descriptions. Emerging patterns and themes were identified using inductive content analysis. Findings: All participants reported an increase in fine motor recovery with their more affected hand. Four primary themes and numerous subthemes emerged from the participants’ interviews. Participants described the ‘Virtual Reality Experience’ (ease of completion; and a novel rehabilitation). They explained the ‘Functional Outcomes’ (increase in confidence; and use of more affected hand) and offered their evaluations for this rehabilitation technique including the ‘Need for Staff’ (one-to-one ratio; guidance and feedback), and recommendations for the ‘Future of Virtual Reality in Stroke Rehabilitation’ (lack of care options; continuation of programming). Conclusion: Commercial gaming and virtual reality were described as an entertaining and novel way to complete rehabilitation for fine motor recovery. Participants’ evaluations of the program included their recommendations for the future of rehabilitation for survivors of chronic stroke in community level care.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".