Stroke survivors' perceptions of a leisure-based virtual reality program
Bibliographic record
Abstract
It is evident that a major issue for stroke survivors is a failure to resume enjoyable leisure activities. This pilot study is a qualitative investigation of the experiences of sixteen stroke survivors, ages 49 to 86, after engaging in a leisure virtual reality (VR) intervention program. Each parti cipant took part in a qualitative in-depth interview to discuss the VR experience. VR can be described as an immersive 3-dimensional environment, which requires participants to interact with simulated objects. Four themes emerged from the data: doing and engaging, enabling competence, this has got me moving, and recommendations. The subtheme technology is okay, also emerged. Findings from this study illustrate that stroke survivors have a decrease in leisure participation and VR is viewed as an opportunity for participation. Engaging in VR creates new interests or rekindles an interest in returning to previous leisure activities. VR is also perceived as a form of therapy with benefits for the body and mind. Moreover, the findings support the idea that older persons are comfortable with this technology. Results are discussed in terms of theories of flow, self-efficacy, and a person-environment experience. Although VR will not fully replace real experiences and environments, it is an avenue for stroke survivors to realize their potential. This research has an implication for practice and future research.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".