Differences in outcomes between the JoyBar control and standard wheelchair joystick control on two maneuverability tasks: a pilot study
Bibliographic record
Abstract
PURPOSE: To determine if older adult, novice wheelchair users who drive a power wheelchair with a JoyBar control complete maneuverability tasks in less time and with less error than those who drive a power wheelchair with a standard joystick control. MATERIALS AND METHODS: A parallel randomized controlled trial design conducted at a medical rehabilitation and research centre with ambulatory older adults aged 60 and above (n = 27). The intervention was the JoyBar alternative wheelchair control. The primary outcome measure was total time to complete each of the two maneuverability tasks. The secondary outcome measure was total number of errors during each of the maneuverability tasks. RESULTS: An independent, two sampled t-test was conducted and revealed that the JoyBar group took a greater amount of time to complete both maneuverability tasks than the control group (p < .05). No significant differences (p < .05) were found in rates of error on either task between the JoyBar and joystick groups. CONCLUSIONS: Maneuverability of a powered wheelchair by novice wheelchair users was not improved through the use of the JoyBar when compared to a standard wheelchair joystick, as measured by rates of error and time to complete maneuverability tasks. Implications for rehabilitation Clients who are new to powered wheelchair use may perform maneuverability tasks faster, with equivalent accuracy, using a standard joystick versus the JoyBar. Clients who use a JoyBar may require adjustments to the programming of their wheelchair to ensure optimal performance. Additional training may be required to achieve proficiency in maneuverability tasks with a JoyBar versus a standard joystick.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".