Physiotherapists’ Experiences Using the Ekso Bionic Exoskeleton with Patients in a Neurological Rehabilitation Hospital: A Qualitative Study
Bibliographic record
Abstract
Use of bionic overground exoskeletons to assist with neurological rehabilitation is becoming increasingly prevalent and has important implications for physiotherapists and their patients. Yet, there is a paucity of research about the impact of integrating this technology on physiotherapists' work. The purpose of this study was to explore how the training and implementation of using the Ekso robotic exoskeleton with patients affects physiotherapists' work. An exploratory qualitative study of three physiotherapists working at a neurological rehabilitation centre in Eastern Canada was conducted using one-on-one semistructured interviews in July 2017. Audio recordings were transcribed verbatim, and data was coded and analyzed using thematic analysis. Six themes emerged from the data: developing organizational capacity; ethical use of technology; benefits of the equipment; challenges of the equipment; cognitive workload; and the technological environment. The results suggest that the adoption and integration of bionic exoskeletons into rehabilitation practice is not as simple as training physiotherapists and giving them the device. More research is needed to understand the increased cognitive demands of working with patients using technologically advanced exoskeletons within a dynamic, technology-rich healthcare environment, while managing patient expectations and ethical use.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".