Mapping the integration of advanced practice physiotherapists in Danish hospitals
Bibliographic record
Abstract
OBJECTIVES: To map the extent of advanced practice physiotherapy (APP) integration in Danish hospitals and explore the roles and characteristics of these models of care. DESIGN: Cross-sectional survey. SETTING: All hospitals in Denmark (secondary care). PARTICIPANTS: Representatives from all 36 Danish hospitals were contacted; 31 responded (86 % response rate). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Prevalence and duration of APP models, departmental placement, scope of responsibilities, and any required education or competencies. RESULTS: Seventeen of the 31 respondent hospitals (55 %) reported having APP roles. The mean duration of APP integration was 9.7 years. Most APP models were located in orthopaedic departments, although they were also present in rheumatology, internal medicine, and acute medical units. Common APP functions included initial patient examination, triage, ordering diagnostic imaging, establishing medical diagnoses, and, in some settings, performing injections or fracture repositioning. Less frequently reported functions included ultrasound examinations and cast application. All respondents noted continuous access to physician consultation. Considerable regional variation was observed in both the departmental integration of APP and the range of responsibilities undertaken by APP clinicians. CONCLUSIONS: Approximately half of Danish hospitals reported established APP roles, indicating broader uptake than previously captured by global surveys. The observed variability across regions and departments underscores the need for standardized guidelines, further research on clinical effectiveness, and clarification of required competencies. Understanding these factors may help optimize APP implementation and improve patient access to timely and effective musculoskeletal and general healthcare.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".