Survivors of chronic stroke – participant evaluations of commercial gaming for rehabilitation
Bibliographic record
Abstract
PURPOSE: There has been an increase in research on the effect that virtual reality (VR) can have on physical rehabilitation following stroke. However, research exploring participant perceptions of VR for post-stroke rehabilitation has been limited. METHOD: Semi-structured interviews were conducted with 10 chronic stroke participants (10 males, mean age = 72.1, mean time since injury = 38.6 mos.) who had recently completed an upper extremity VR stroke rehabilitation programme. RESULTS: Four main themes emerged: 'the VR experience,' 'functional outcomes,' 'instruction,' and the 'future of VR in stroke rehabilitation,' along with nine sub-themes. Participants illustrated the positive impact that VR training had on their functional abilities as well as their confidence towards completing activities of daily living (ADL). Participants also expressed the need for increased rehabilitation opportunities within the community. CONCLUSION: Overall, participants were optimistic about their experience with VR training and all reported that they had perceived functional gain. VR is an enjoyable rehabilitation tool that can increase a stroke survivor's confidence towards completing ADL. Implications for Rehabilitation Although there is an increase in rehabilitation programmes geared towards those with chronic stroke, we must also consider the participants' perception of those programmes. Incorporating participant feedback may increase enjoyment and adherence to the rehabilitation programmes. The VR experience, as well as provision of feedback and instruction, are important aspects to consider when developing a VR programme for stroke survivors. VR for rehabilitation may be a feasible tool for increasing the survivors' confidence in completing ADL post-stroke.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".