Social impact of the JACO wheelchair-mounted robotic arm on users and their caregivers
Bibliographic record
Abstract
Abstract The efficacious implementation of robotic assistive technologies must be built on a thorough understanding of stakeholders’ experiences and perceptions. This study provides an in-depth insight into the experiences and perceptions of users of the JACO wheelchair-mounted robotic arm and those of their caregivers. A sample of JACO users (n = 21; Female : 6; Male : 15) and caregivers (n = 11; Female: 2; Male: 9) participated in individual interviews used to gain qualitative insight into the impact of JACO on their day-to-day lives. Interview transcripts were analyzed using a hybrid deductive-inductive coding process. Thematic analysis was conducted in accordance with the Consortium on Assistive Technology Outcomes Research (CATOR) taxonomy. This article exclusively reports data on the social impact of the JACO wheelchair-mounted robotic arm. In addition, participants completed three questionnaires to gather more objective data for quantitative assessment, these included the Caregiver Assistive Technology Outcome Measure (CATOM), a sociodemographic questionnaire, and a home-based questionnaire to assess the social impact of using JACO. Findings pointed to highly varied experiences among participants, including instances of positive, negative, and absence of effects from the use of JACO. Participants’ feedback fell within two broad categories, Human Assistance, and Cost and Use of Resources . This study provides nuanced and varied insight into the spectrum of the social impact of using JACO as perceived by users and their caregivers, highlighting the importance of considering each user as an individual with unique experiences and needs. Continued research is needed to assess the generalizability of these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".