Wheelchair Skills Test Outcomes across Multiple Wheelchair Skills Training Bootcamp Cohorts
Bibliographic record
Abstract
User training is a critical component of wheelchair service delivery to ensure individuals with a mobility impairment can negotiate environmental barriers and promote their social participation. A wheelchair “bootcamp”, delivered during professional preparation education, is one strategy to better prepare occupational therapists for clinical rehabilitation practice by developing their own wheelchair skills. The purpose of this study was a retrospective review of a large dataset of student cohorts from a single site and delineate bootcamp effects on the Wheelchair Skills Test-Questionnaire (WST-Q) scores. Participant data from eight cohorts was consolidated (n = 307). Comparison of two WST-Q scoring formats revealed significantly lower scores for cohorts using the 4-point version, which was subsequently standardized to the other 3-point version. WST-Q change scores were similar between cohorts, and differences were more reflective of variability in skill level prior to bootcamp than post-bootcamp scores. Students were able to master most basic and intermediate level skills, while advanced skill acquisition was much more variable. This study provides more precise point estimates of wheelchair skill acquisition among occupational therapy students than previous studies. While confirming the benefits of bootcamp education, recommendations for further investigation were identified.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".