A Remote-Learning Course can improve the subjective wheelchair-skills performance and confidence of wheelchair service providers: an observational cohort study
Bibliographic record
Abstract
Purpose To test the hypothesis that a Remote-Learning Course improves the subjective wheelchair-skills performance and confidence of wheelchair service providers, and to determine the participants’ views on the Course.Methods This was an observational cohort study, with pre-post comparisons. To meet the objectives of the six-week Course, the curriculum included self-study and weekly one-hour remote meetings. Participants submitted their Wheelchair Skills Test Questionnaire (WST-Q) (Version 5.3.1) “performance” and “confidence” scores before and after the Course. Participants also completed a Course Evaluation Form after the Course.Results The 121 participants were almost all from the rehabilitation professions, with a median of 6 years of experience. The mean (SD) WST-Q performance scores rose from 53.4% (17.8) pre-Course to 69.2% (13.8) post-Course, a 29.6% relative improvement (p < 0.0001). The mean (SD) WST-Q confidence scores rose from 53.5% (17.9) to 69.5% (14.3), a 29.9% relative improvement (p < 0.0001). Correlations between performance and confidence were highly significant (p < 0.0001). The Course Evaluation indicated that most participants found the Course useful, relevant, understandable, enjoyable, “just right” in duration, and most stated that they would recommend the Course to others.Conclusions Although there is room for improvement, a Remote-Learning Course improves the subjective wheelchair-skills performance and confidence scores of wheelchair service providers by almost 30%, and participants were generally positive about the Course.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".