Training Multitasking in a Virtual Supermarket: A Novel Intervention After Stroke
Bibliographic record
Abstract
OBJECTIVE: To explore the potential of the VMall, a virtual supermarket running on a video-capture virtual reality system, as an intervention tool for people who have multitasking deficits after stroke. METHOD: Poststroke, 4 participants received ten 60-min sessions over 3 weeks using the VMall. The intervention focused on improving multitasking while the participant was engaged in a virtual shopping task. Instruments included the Multiple Errands Test-Hospital Version (MET-HV) in a real mall and in the VMall. RESULTS: Participants achieved improvements ranging from 20.5% to 51.2% for most of the MET-HV measures performed in a real shopping mall and in the VMall. CONCLUSIONS: The data support the VMall's potential as a motivating and effective intervention tool for the rehabilitation of people poststroke who have multitasking deficits during the performance of daily tasks. However, because the sample was small, additional intervention studies with the VMall should be conducted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".