Patient-Reported Experiences of Musculoskeletal Virtual Care Delivered by Advanced Practice Physiotherapists
Bibliographic record
Abstract
Purpose: To better understand patients' perspectives on virtual care (VC) delivered by advanced practice physiotherapists (APPs) for hip/knee, foot/ankle, shoulder/elbow, and low back related symptoms. Method: A patient satisfaction questionnaire was developed and distributed electronically to all patients seen by APPs from August 1, 2020 to January 31, 2021. The questionnaire contained quantitative items using a 5-point Likert scale and open-ended questions that yielded qualitative findings. Descriptive statistics were applied to the quantitative data. Qualitative findings were analyzed using a qualitative description approach to identify recurrent themes. Results: Response rate was 74% (374/505) across all clinics. Videoconference was the most common delivery method (91.7%). Overall satisfaction with VC was very high (4.7-4.8/5). Emergent qualitative themes were related to Personal Connection; Preparatory Materials; Virtual Physical Examination; Practical Advantages of VC; Virtual Waiting Room; and Technical Issues. Conclusions: Overall, across several facets including personal connection, patient experience with VC for a variety of musculoskeletal conditions was rated high. Clinically, a systematic approach to the physical examination with preparatory patient education materials was key to positive patient experience.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".