Outcomes of intervention programs using flatscreen virtual reality
Bibliographic record
Abstract
Virtual reality (VR) has the potential to offer experiences which are engaging and rewarding. In VR, the focus is shifted from the person's efforts in producing a movement or completing a task to that of interaction with the virtual environment. We have found that participants place value and meaning on and enjoy the activities programmed. Virtual reality interventions have been shown to improve cognitive function and concentration through an individual's interaction with a pleasant activity. Importantly, the enjoyment experienced while working with VR may increase the level of participation. In addition to generating realistic situations for testing, intervention and collection of data, the provision of immediate and positive feedback through VR has been shown to increase self esteem and empowerment. We will report outcomes from several intervention and feasibility trials using a flat screen virtual reality system with survivors of traumatic brain injury, community living older adults and children with spastic cerebral palsy. Gross motor movements were elicited through various game-like VR applications without the need for head-mounted displays or other peripherals. The impact of VR exercise participation ranged from improvements in clinical measures of functional balance and mobility, time on task, as well as participant and care provider perceptions of enjoyment, independence and confidence. Although still preliminary, our data suggest that simple applications of virtual reality have significant impacts on physical and psychosocial variables. Possibilities for and benefits of home and community-based access to virtual reality based programs will be explored.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".