Knee Osteoarthritis: An Investigation into the Clinical Practice of Physiotherapists in Canada
Bibliographic record
Abstract
Purpose: The purpose of this study was to establish the clinical practice of physiotherapists who treat people with knee osteoarthritis (OA) in Canada and examine their knowledge, awareness, use of, and attitudes toward clinical practice guidelines (CPGs). Method: We conducted a cross-sectional survey of physiotherapists who are licensed to practise in Canada and who treat people with knee OA. Results: A total of 388 physiotherapists completed our survey. Approximately two-thirds of them (271) were aware of CPGs. Out of all CPG recommendations, most respondents provided leg strengthening exercises (380) and education (364). More than 80% believed that CPGs improved patient care and enhanced decision making and were confident in their ability to interpret CPGs. More physiotherapists (204) identified barriers to the use of CPGs than facilitators of their use (117). Physiotherapists who were employed in private practice were substantially more likely to use interventions such as acupuncture (odds ratio [OR] 5.98; 95% CI: 2.92, 12.23; p < 0.01) and joint mobilization and manipulation (OR 6.58; 95% CI: 3.45, 12.55; p < 0.01) than were physiotherapists employed in hospital settings. Conclusions: Two-thirds of respondents were aware of CPGs. Physiotherapists provided education and leg strengthening exercises more often than aerobic exercise and weight management advice. Physiotherapists employed in private practice were more likely to use adjunct interventions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".