Advanced Practice Physiotherapy in Canada: A Survey of Canadian Physiotherapists
Bibliographic record
Abstract
Purpose: Advanced practice physiotherapy (APP) represents an important development for the physiotherapy profession worldwide but few studies have documented these models in Canada and the physiotherapists working in such models. The objective of this study was to identify and describe Canadian physiotherapists in APP roles and their models of care (MoC) and identify barriers and facilitators of APP development in Canada. Method: An electronic survey was sent to Canadian physiotherapists with the collaboration of various professional organizations. The questionnaire included 37 questions about APP roles and MoC. Results: Fifty-seven physiotherapists identified themselves as APPs and completed the survey. Most practised in private clinics (58.1%) or outpatient orthopaedic clinics (27.9%) and provided care to adults (95.3%) with orthopaedics disorders (86.0%). Most APPs were involved in first-contact and leading overall care (52.3%) or triage roles where they identify surgical candidates (68.2%). APPs mentioned their roles were established to improve care efficiency (82.1%) and were viewed positively by medical teams (76.9%) and patients (76.9%). Professional regulations (32.4%) and funding of roles and models (24.3%) were identified as barriers. Conclusions: Results of this study provide new original data regarding APP practice and MoC in Canada, acknowledging the various APP MoC and roles of respondents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".