Incorporating Research Technology into the Clinical Assessment of Balance and Mobility: Perspectives of Physiotherapists and People with Stroke
Bibliographic record
Abstract
PURPOSE: To describe the perspectives of people with stroke and their physiotherapists on the use of biomechanics technology to assess balance and mobility. METHODS: This qualitative study used semi-structured interviews with patients with stroke and a focus group with their physiotherapists. Coding of interview and focus-group data used a line-by-line inductive approach, with qualitative software to develop codes into themes. RESULTS: The quantitative data from the assessment were seen as beneficial to providing patients with insight into balance and mobility problems. Physiotherapists found that the assessment confirmed clinical reasoning and aided in precise evaluation of progress but expressed mixed opinions as to whether treatment choice was influenced. Patients would have liked more communication regarding the purpose of the assessment. Patients also stated that trust in their physiotherapists helped them overcome anxieties and that confidence was gained through exposure to more challenging balance assessments. Physiotherapists advocated for the use of a harness system to safely incorporate reactive balance control assessment and training into practice. CONCLUSION: Both patients and therapists saw value in the quantitative data provided by the assessment. Regardless of the technology used, patients value a strong physiotherapist-patient relationship. Ongoing collaboration between clinicians and researchers should guide the evolution of technology into clinically useful tools.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".