Outcomes in occupational therapy students’ preparation for wheelchair skills training provision
Bibliographic record
Abstract
While wheelchair skills training has demonstrated highly effective outcomes for wheelchair users, prevalence of receiving comprehensive skills training is low. Studies demonstrate a wheelchair skills "bootcamp" significantly improves occupational therapy students' capacity to demonstrate wheelchair skill performance; however, how bootcamps impact students' self-efficacy to deliver skills training in future clinical practice is unclear. This study explored a large dataset collected from nine successive student cohorts attending a structured wheelchair skills bootcamp at a single site. Bootcamps were 4-4.5 hours in duration and content was based on the Wheelchair Skills Program. Mean improvement in skill capacity was 34.8% (95% CI 33.5; 36.1) and wheelchair self-efficacy improved by 28.7% (95% CI 27.3; 30.1). Post-bootcamp self-efficacy scores for Assessment (80.9%), Training (78.5%), Spotting (87.4%), and Documentation (70.4%) all improved by 30-40%. Mandatory bootcamps had lower baseline scores but similar post-bootcamp and change scores as voluntary ones. Cohorts during the COVID-19 pandemic had significantly lower baseline scores for wheelchair skill capacity and confidence as well as self-efficacy with assessment, but significantly larger improvements post-bootcamp. An experiential bootcamp is effective across a wide range of occupational therapy student cohorts in preparing them to deliver wheelchair skills training in future clinical practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".