The Effect of Rolling Resistance on Stationary Wheelchair Wheelies
Bibliographic record
Abstract
OBJECTIVE: To test the hypotheses that increased rolling resistance (RR) reduces rear-wheel displacement and perceived difficulty during the takeoff and balance phases of stationary wheelchair wheelies. DESIGN: We carried out within-subject comparisons of 20 participants as they each performed, in random order, two 30-sec stationary wheelies in three RR settings (tile, 5-cm-thick foam, and 12.5-cm-high blocks in front of and behind the rear wheels). The main outcome measures were rear-wheel displacement (in centimeters for the takeoff phase and centimeters per second for the balance phase) from a spring-loaded potentiometer and Likert scales of perceived difficulty. RESULTS: For rear-wheel displacement, all six of the pairwise comparisons (three terrains x two phases (takeoff and balance)) showed a significant statistical difference (P < 0.002). In each of the six pairwise comparisons, displacement was less for the higher of the two RR conditions. For perceived difficulty, during the balance phase, participants perceived tile to be significantly more difficult than either foam (P = 0.0067) or blocks (P = 0.0002). The other pairwise comparisons were not statistically significant. CONCLUSION: In conditions of increased RR, rear-wheel displacement and perceived difficulty are reduced during stationary wheelchair wheelies. These findings have implications for teaching wheelchair users to perform wheelies, a foundation of many advanced wheelchair skills.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".