Impacts of the Jaco robotic arm on user satisfaction with performance in everyday activities
Bibliographic record
Abstract
Assistive technology (AT) such as the Jaco robotic arm (JRA) provides significant benefits for individuals with upper extremity disabilities (UED). Despite these advantages, barriers including funding limitations, lack of awareness, and insufficient evidence of efficacy can hinder access. This study examined the effectiveness of the JRA by evaluating user performance and satisfaction with occupational performance (OP) before and after device use. A non-experimental retrospective mixed-method study was employed. Participants completed interviews via Zoom using the Canadian Occupational Performance Measure (COPM). Interview data were analyzed and member checking was conducted to ensure accuracy. A convenience sample of JRA users in North America, aged 18 years and older, was recruited in collaboration with Partners in Medicine, the device's North America distributor. The COPM identified challenges in self-care, productivity, and leisure, as well as corresponding performance and satisfaction ratings prior to and following JRA use. Twelve users, aged 30-58, who had used the JRA for 3 weeks to 8 years, participated. Common OP problems included opening and closing doors, retrieving items, drinking, manipulating phones, eating, community mobility, and repositioning body parts. Participants consistently reported meaningful improvements after acquiring the JRA. Individual average performance score change ranged from 3.2 to 8.3, while satisfaction scores ranged from 3.0 to 7.7. This study demonstrated that the JRA was a beneficial AT intervention for study participants.
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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 | 0.002 | 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) | 0.002 | 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".