Video analysis of communication by physiotherapists and patients in video consultations: a qualitative study using conversation analysis
Bibliographic record
Abstract
OBJECTIVES: To investigate the challenges of doing physical examinations and exercises by video, and the communication strategies used by physiotherapists and patients to overcome them. DESIGN: A qualitative study of talk and social actions, examining the verbal and non-verbal communication practices used by patients and physiotherapists. Video consultations between physiotherapists and patients were video recorded using MS Teams, transcribed and analysed in detail using Conversation Analysis. SETTING: Video consultations were recorded in three specialist settings (long-term pain, orthopaedics, and neuromuscular rehabilitation) across two NHS hospitals. PARTICIPANTS: 15 adult patients (10 female, 5 male; aged 20-77) with a scheduled video consultation. RESULTS: Examinations and exercises retain-->were successfully accomplished in all 15 consultations. Two key challenges were identified for physiotherapists and patients when doing video assessments: (1) managing safety and clinical risk, and (2) making exercises and movements visible. Challenges were addressed by through communication practices that were patient-centred and tailored to the video context (e.g., explaining how to frame the body to the camera or adjust the camera to make the body visible). CONCLUSIONS: Video is being used by physiotherapists to consult with their patients. This can work well, but tailored communication strategies are critical to help participants overcome the challenges of remote physical examinations and exercises. CONTRIBUTION OF THE PAPER: This paper is a first to use video-based analysis to determine the challenges of video consulting for doing remote assessments and exercises in physiotherapy settings. It demonstrates how patients and physiotherapists use communication strategies to raise concerns around safety and visibility and how they overcome these concerns.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".