Acceptability of Physiotherapists in the Emergency Department for the Care of Adults With Musculoskeletal Disorders
Bibliographic record
Abstract
Purpose: Over the past decades, pressure on emergency departments (EDs) has been increasing. New ED models of care including physiotherapists in more autonomous roles, often called advanced practice physiotherapy (APP) care, are emerging to improve access to care, especially for patients with musculoskeletal disorders (MSKDs). As such, the purpose of this study was to assess patient' acceptability of APP ED care for patients with MSKDs. Methods: Patients consulting for an MSKD were recruited in 1 Canadian ED and completed a 13-question survey assessing their acceptability of ED APP care. Descriptive analyses as well as χ2 and Fisher's exact tests, with associated 95% confidence interval, were performed. Results: Forty-one patients completed the survey. A majority of respondents (56%) trusted APPs to provide accurate diagnoses for MSKD in the ED, and 80.5% were confident they would provide safe care. Most participants felt confident that APPs would appropriately order medical imaging tests (73%) or prescribe medication (66%) when necessary. Sixty-six percent of participants agreed that seeing only a physiotherapist without the intervention of a physician would reduce their length of ED stay. Conclusions: Within this exploratory survey, participants were favorable to ED APP for the care of MSKD, suggesting that implementation of such models would be accepted by patients with MSKD presenting to an ED.
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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 | 0.021 |
| 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) | 0.005 | 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".