Virtual community centre for power wheelchair training: Experience of children and clinicians
Bibliographic record
Abstract
PURPOSE: To: 1) characterize the overall experience in using the McGill immersive wheelchair - community centre (miWe-CC) simulator; and 2) investigate the experience of presence (i.e., sense of being in the virtual rather than in the real, physical environment) while driving a PW in the miWe-CC. METHOD: A qualitative research design with structured interviews was used. Fifteen clinicians and 11 children were interviewed after driving a power wheelchair (PW) in the miWe-CC simulator. Data were analyzed using the conventional and directed content analysis approaches. RESULTS: Overall, participants enjoyed using the simulator and experienced a sense of presence in the virtual space. They felt a sense of being in the virtual environment, involved and focused on driving the virtual PW rather than on the surroundings of the actual room where they were. Participants reported several similarities between the virtual community centre layout and activities of the miWe-CC and the day-to-day reality of paediatric PW users. CONCLUSION: The simulator replicated participants' expectations of real-life PW use and promises to have an effect on improving the driving skills of new PW users. Implications for rehabilitation Among young users, the McGill immersive wheelchair (miWe) simulator provides an experience of presence within the virtual environment. This experience of presence is generated by a sense of being in the virtual scene, a sense of being involved, engaged, and focused on interacting within the virtual environment, and by the perception that the virtual environment is consistent with the real world. The miWe is a relevant and accessible approach, complementary to real world power wheelchair training for young users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".