Comparison between performance with a pushrim-activated power-assisted wheelchair and a manual wheelchair on the Wheelchair Skills Test
Bibliographic record
Abstract
PURPOSE: To test the hypothesis that people using a pushrim-activated power-assisted wheelchair (PAPAW) can accomplish a wider range of wheelchair skills than when using a manual wheelchair (MWC). METHODS: We studied 30 able-bodied participants, using within-participant comparisons. Participants used a manual wheelchair equipped with both PAPAW and regular MWC rear wheels, and rear anti-tip devices (Arc-RADs) that permitted wheelie-like function. We trained participants to perform the wheelchair skills of the Wheelchair Skills Training Program (WSTP, Version 2.4). From the Wheelchair Skills Test (WST, Version 2.4), we calculated pass-fail success rates for the 50 individual skills and a total percentage WST score. RESULTS: The mean (+/-SD) total WST scores were 89.3 (+/-7.0)% for the PAPAW and 88.8 (+/-8.4)% for the MWC, with a mean difference of 0.6 (+/-5.6)% (p = 0.59). Qualitative observations suggested that skills requiring a higher force on the pushrim (e.g., incline ascent) were performed more easily with the PAPAW, whereas skills requiring greater control of the wheelchair (e.g., wheelie-dependent skills) were performed more easily with the MWC. CONCLUSION: Overall wheelchair skill performance with the PAPAW is not superior to that when using the MWC. The PAPAW may be helpful for specific skills that require more wheel torque, but the additional torque appears to be disadvantageous when performing skills that require greater control.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".