Cross-sectional UK survey of advanced practice physiotherapy: characteristics and perceptions of existing roles
Bibliographic record
Abstract
Background/aims Few studies have investigated the characteristics of advanced practice physiotherapy in the UK to evaluate current context and implementation. The aim of this study was to understand how advanced practice physiotherapy is implemented in the UK. Methods A cross-sectional online descriptive national questionnaire was developed, using a previous survey and literature, comprising 33 closed, Likert-scale and open questions. Data analysis was undertaken using frequencies and thematic analysis. All 646 members of the Advanced Practice Physiotherapy Network were invited to participate. Results A total of 142 (22% response) reported 13 job titles; 40% had experienced ≥1 title change; most (50.7%) preferred ‘advanced practice physiotherapist’. High level job satisfaction was identified but barriers prevent fulfilment of the four advanced practice physiotherapy pillars: clinical practice, leadership and management, education and research. High level clinical skills and facilitating patient pathways were key to role differentiation. Problems included lack of support, inconsistency between responsibility and reward, and no consistent framework for advanced practice physiotherapy roles. Conclusions Advanced practice physiotherapists are largely satisfied, but significant variation exists in titles and implementation of roles. A consistent advanced practice physiotherapy framework is required.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".