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Record W4394727797 · doi:10.3138/ptc-2023-0058

Knowledge and Care Quality of Physiotherapy Technologists in the Management of Common Shoulder Disorders: Results from a Survey in the Province of Quebec, Canada

2024· article· en· W4394727797 on OpenAlexaffvenueabout
Annie Bélanger, Véronique Lowry, François Desmeules

Bibliographic record

VenuePhysiotherapy Canada · 2024
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de l'Est-de-l'Île-de-MontréalUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPhysical therapyMedicineQuality (philosophy)Family medicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.352
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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