Patients presenting with musculoskeletal disorders in the emergency department: A qualitative study of their experiences when cared by advanced practice physiotherapists in the province of Québec
Bibliographic record
Abstract
BACKGROUND: Advanced practice physiotherapy (APP) models of care are promising to alleviate pressure in emergency departments (EDs) where physiotherapists' new roles include being a first-contact practitioner and leading the overall care and management of patients with minor musculoskeletal disorders (MSKDs) to alleviate ED physicians' caseload. PURPOSE: To explore patients' acceptability, experience, satisfaction, and perception of a new APP-led model of care in the ED. METHODS: Patients presenting to the ED with a minor MSKD and who agreed to participate in a multicenter, pan-Canadian randomized controlled trial assessing the efficacy and costs of an APP model of care were invited to participate in this qualitative study. Semi-structured interviews were performed to identify themes related to their experiences with this model. Verbatim transcripts were coded and analysed using an inductive thematic analysis. RESULTS: 11 patients participated and three themes were identified: 1- They were satisfied with the care received within the model; 2- They found APPs to have the appropriate skill set to manage MSKDs and to assume medical-delegated tasks; 3- Timely access to care was a key factor in the acceptability of this model and participants believed physiotherapists were appropriate first-contact practitioners. One participant proposed that the APP model of care should also offer follow-up care. CONCLUSION: Participants had a positive experience of care in this new model. These results support the implementation of APP models of care in EDs as the participants appear receptive to new roles for APPs.
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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".