Clinical stakeholders’ perspective for the integration of an immersive wheelchair simulator as a clinical tool for powered wheelchair training
Bibliographic record
Abstract
This study aimed to investigate clinical stakeholders' acceptance of an immersive wheelchair simulator as a potential powered wheelchair skills training tool. Focus groups, conducted in four rehabilitation centers, were used to obtain a rich understanding of participants' experiences and beliefs. Then, a cross-sectional survey of the simulator acceptability for clinical practice was created. Twenty-three rehabilitation therapists and clinical program directors participated in the focus groups and thirty-three responded to the survey. Participants generally expressed that use of the simulator would be complementary to training in an actual powered wheelchair, and that it could be useful for challenging situations in rehabilitation centers (e.g. anxious clients; when there is uncertainty around their potential to drive a powered wheelchair; tasks that cannot be assessed in a real-life environment). They also provided suggestions to improve the simulator (e.g. more feedback during tasks; possibility of adjusting control settings such as speed and sensitivity; possibility of adding varied control interfaces). Feedback received from key stakeholders clearly indicated that the wheelchair simulator would be complementary to training provided in a real context of use. However, some important limitations must be addressed to improve the simulator and promote its adoption by clinical programs, therapists and clients.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".