Knowledge and Care Quality of Physiotherapy Technologists in the Management of Common Shoulder Disorders: Results from a Survey in the Province of Quebec, Canada
Bibliographic record
Abstract
Purpose: In Québec, physiotherapy technologist (Phys.T.) scope of practice allows them to complete the evaluation and treat various musculoskeletal disorders, including shoulder disorders, after an initial assessment by a referring provider. They may need to re-evaluate and refer back to the providers if a patient does not progress in a satisfactory manner. Our purpose is to evaluate knowledge and care of practicing Phys.T. in identifying and managing overall care for common shoulder disorders. Method: A survey presented four clinical vignettes featuring common shoulder disorders. Survey participants provided information regarding diagnosis, imaging recommendations, specialist referrals, medical and rehabilitation care, and their confidence in managing these clinical cases. Responses were compared to recommendations from selected clinical practice guidelines (CPGs). Results: 43 Phys.T. completed the survey, with the majority accurately identifying common shoulder disorders across all vignettes (74%-94%). Compliance with CPGs was observed for rotator cuff tendinopathy (60%) and adhesive capsulitis (61%), with most Phys.T. refraining from initial imaging tests. However, a significant proportion recommended imaging for acute full-thickness rotator cuff tear (52%) and recurrent traumatic glenohumeral instability (80%), in line with CPGs. Education and exercises were prioritized in all vignettes as per CPGs, although a proportion favored passive physical modalities not endorsed by CPGs (13%-72%). Conclusion: Most Phys.T. demonstrated adequate identification and management of shoulder disorders, reflecting their collaborative role in patient care. However, discrepancies existed in adherence to evidence-based recommendations, suggesting the need for additional training to optimize care pathways and inter-professional collaborations for shoulder and musculoskeletal disorders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".