Active Video Games and Low-Cost Virtual Reality: An Ideal Therapeutic Modality for Children With Physical Disabilities During a Global Pandemic
Bibliographic record
Abstract
Current guidelines against spread of coronavirus (COVID-19) interrupt non-essential rehabilitation services. Thus, individuals with physical disabilities such as children with cerebral palsy can no longer benefit from physical rehabilitation during this undetermined period. Using either a synchronous or asynchronous format, in collaboration with a therapist via telerehabilitation, we suggest that active video games and low-cost virtual reality are a promising delivery mode for at-home rehabilitation in the context of a global pandemic. This therapeutic modality, incorporated into an at-home individualized treatment plan, provides a means to lessen the impact of an interruption in rehabilitation services while not loosing the pre-pandemic, in-person physical activity gains. Growing evidence supports active video games and low-cost virtual reality as viable therapeutic interventions for children with physical disabilities. These technologies are especially well-accepted by pediatric populations for the ludic and motivating features that lend themselves to nearly seamless incorporation into telerehabilitation. Advantages for rehabilitation of active video games and low-cost virtual reality include a rich, challenging, multi-modal training environment in which high numbers of movement repetitions can be accomplished, and a unique opportunity to foster engaged practice actions that go beyond household activities. We offer suggestions for the clinician about how to adopt active video games and low-cost virtual reality into your practice during a global pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".