“ <i>Rock on technology</i> ”: perspectives of people with neurological conditions on robot-assisted lower limb and gait neurorehabilitation
Bibliographic record
Abstract
Purpose Explore perspectives of people living with neurological conditions in Western Australia (WA) on robot-assisted lower limb neurorehabilitation (RALLR), and implementation requirements.Materials and methods This co-designed, qualitative descriptive study included people living with neurological conditions. In-person semi-structured focus groups (FGs) were facilitated by a person with lived experience of stroke. FGs were recorded, data were transcribed, and thematically analysed using a reflexive approach.Results Five FGs included 24 participants (54.2% women, median age 50 years). Of these, thirteen participants had RALLR experience. Primary neurological conditions represented among participants comprised spinal cord injury (n = 11), stroke (n = 3), multiple sclerosis (n = 2), and other conditions (n = 8). Three main themes were established: perceived benefits (physiological, psychosocial, therapy, ambulation, independence, and pain), barriers (awareness, access, cost, psychological challenges, and device limitations), and recommendations for future implementation in WA (access, design, and purpose of robotic devices).Conclusions This study highlights the desire for improved access to RALLR among people living with neurological conditions in WA. Participants acknowledged multiple benefits of RALLR; however, addressing financial, design, and availability barriers of RALLR are necessary for successful adoption. Future efforts should prioritise accessibility in both metropolitan and regional areas, optimise device usability, and foster interdisciplinary collaboration in RALLR integration.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".