Knowledge and appropriateness of care of family physicians and physiotherapists in the management of shoulder pain: a survey study in the province of Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Shoulder pain is difficult to diagnose and treat with half of those affected still symptomatic six months after initial consultation. This may be explained by primary care management not conforming to evidence-based practice. This survey evaluated physiotherapists (PTs) and family physicians' (FPs) knowledge and appropriateness of care in shoulder pain management. METHODS: A survey sent to PTs and FPs in the province of Quebec, Canada presented four clinical vignettes with cases of rotator cuff (RC) tendinopathy, acute full-thickness RC tear, adhesive capsulitis and traumatic anterior glenohumeral instability. Respondents indicated diagnosis, indications for imaging, specialists' referrals, and choice of treatments. Answers were compared to recommendations from clinical practice guidelines (CPGs). Participants' responses were compared between types of providers with Fisher's exact test. RESULTS: Respondents (PTs = 175, FPs = 76) were mostly women with less than ten years of experience. More than 80% of PTs and 84% of FPs correctly diagnosed cases presented. Despite this practice not being recommended, more FPs than PTs recommended an imaging test in the initial management of RC tendinopathy (30% compared to 13%, p = 0.001) and adhesive capsulitis (51% compared to 22%, p = 0.02). For full-thickness RC tear and shoulder instability, up to 72% of FPs and 67% of PTs did not refer to a specialist for a surgical opinion, although recommended by CPGs. For RC tendinopathy, 26% of FPs and 2% of PTs (p < 0.001) would have prescribed a corticosteroid infiltration, which is not recommended in the initial management of this disorder. For adhesive capsulitis, significantly more FPs (76%) than PTs (62%) (p < 0.001) suggested an intra-articular corticosteroid infiltration, as recommended by CPGs. For all presented vignettes, up to 95% of family physicians adequately indicated they would refer patients for physiotherapy. In prioritizing rehabilitation interventions, up to 42% of PTs did not consider active exercises as a priority and up to 65% selected passive modalities that are not recommended for all shoulder pain vignettes. CONCLUSIONS: Most FPs and PTs were able to make adequate diagnoses and select appropriate treatments for shoulder pain, but practices opposed to evidence-based recommendations were chosen by several respondents. Further training of FPs and PTs may be needed to optimize primary care management of different shoulder disorders.
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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 | 0.000 | 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 | 0.000 |
| 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".