Effect of travel direction and wheelchair position on the ease of a caregiver getting an occupied wheelchair across a soft surface: a randomized crossover trial
Bibliographic record
Abstract
Purpose To test the hypotheses that, in comparison with pushing an occupied upright manual wheelchair forward, pulling backward on the push-handles improves the objective and subjective ease with which a caregiver can get the wheelchair across a soft surface (e.g., grass, mud, sand, gravel); and the ease with which a caregiver can get the wheelchair across a soft surface improves if the wheelchair is tipped back into the wheelie position.Methods We used a randomized crossover trial with within-participant comparisons to study 32 able-bodied pairs of simulated caregivers and wheelchair occupants. The caregiving participants moved an occupied manual wheelchair 5 m across a soft surface (7.5-cm-thick gym mats) under four conditions (upright-forward, upright-backward, wheelie-forward and wheelie-backward) in random order. The main outcome measure was time (to the nearest 0.1 s) and the main secondary measure was the ease of performance (5-point Likert scale).Results The upright-backward condition was the fastest (p < 0.05) and had the highest ease-of-performance scores. In the forward direction, there was no statistically significant difference in the time required between the upright and wheelie positions, but the wheelie position was considered easier.Conclusions Although further study is needed, our findings suggest that caregivers should pull rather than push occupied wheelchairs across soft surfaces. In the forward direction, caregivers may find the wheelie position easier than the upright condition. These techniques have the potential to both improve the effectiveness of and reduce injuries to caregivers. Clinical Trial Registration Number: NCT 04998539
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".