Simulated real-world feasibility and feedback session for a lift assistance device, Raymex™: a mixed-method descriptive study
Bibliographic record
Abstract
Background: Fall risk and incidence increase with age, creating significant physical and mental burden for the individual and their care provider. Lift assistive devices are used in multiple healthcare facilities, but are generally not portable nor self-operational, limiting their use outside of medical supervision. The Raymex™ lift is a novel lift assistance device within a rollator to address these limitations. We aim to gather user-centered feedback on the Raymex™ lift, set up instructions, safety protocols to improve feasibility and usability, and explore the potential usability as a fall recovery or prevention device. Methods: Four older adults, two informal caregivers and 16 formal caregivers (clinicians and continuing care assistants) participated in a focus group. Participants provided feedback on the Raymex™ lift after viewing a demonstration and using the device. Qualitative and quantitative data were analysized using thematic and descriptive analysis respectively. Results: Participants highlighted three major themes: (1) Design features requiring improvement, (2) Positive feedback and suggestions to optimize the Raymex™ lift and (3) Pricing vs. social utility. Participants suggested widening the seat, changing the braking button layout, and lowering the device weight to improve usability. Participants believed the main device feature was fall recovery and had implications for social utility by reducing the need for ambulance visits to the home. Price point led to a concern on affordability for older adults. Conclusion: The feedback gained will advance the development of the Raymex™ lift and may highlight cost-effective design choices for other developers creating related aging assistive technologies.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".