Development of a virtual reality-based intervention for community walking post stroke: an integrated knowledge translation approach
Bibliographic record
Abstract
PURPOSE: To develop a virtual reality (VR) based intervention targeting community walking requirements. METHODS: Two focus groups each involving 7 clinicians allowed exploring optimal features, needed support and perceived favorable/unfavorable factors associated with the use of the VR-based intervention from the clinicians' perspective. Three stroke survivors and 2 clinicians further interacted with the intervention and filled questionnaires related to acceptability and favorable/unfavorable perceptions on the VR intervention. Stroke participants additionally rated their perceived effort (NASA Tax Load Index), presence (Slater-Usoh-Steed) and cybersickness (Simulator Sickness Questionnaire). RESULTS: Results identified optimal features (patient eligibility criteria, task complexity), needed support (training, human assistance), as well as favorable (cognitive stimulation, engagement, representativeness of therapeutic goals) and unfavorable factors (misalignment with a natural walking pattern, client suitability, generalization to real-life) associated with the intervention. Acceptability scores following the interaction with the tool were 28 and 42 (max 56) for clinicians and ranged from 43 to 52 for stroke participants. Stroke participants reported moderate perceptions of effort (range:20-33/max:60), high levels of presence (29-42/42) and minimal cybersickness (0-3/64). CONCLUSION: Findings collected in the early development phase of the VR intervention will allow addressing favorable/unfavorable factors and incorporating desired optimal features, prior to conducting effectiveness and implementation studies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".