Developing a core competency and capability framework for advanced practice physiotherapy: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: There is an urgent need to develop an international competency and capability framework to support standardization of education and roles in advanced practice physiotherapy (APP). This need arose due to the rapid growth of the APP model of care, implemented out of necessity in the absence of agreement as to the competencies and capabilities or formal education required for the roles. This study explores the views and perceptions of practitioners and key stakeholders on a draft competency and capability framework for advanced practice physiotherapists. OBJECTIVES: The purpose of this study was to: 1) gather feedback from key stakeholders (advanced practice physiotherapists, researchers, and leaders) on a draft competency and capability framework and 2) use that feedback to revise and improve the draft framework. DESIGN: Qualitative study using a series of four multi-national online focus groups. Thematic analysis was conducted according to Braun and Clarke. RESULTS: Sixteen participants from the United Kingdom, Ireland, Canada, Australia, and New Zealand participated in the study. Five themes were generated after data analysis: clinical expert, experienced communicator, strong leader, collaborator, and knowledge creator). A modified competency and capability framework was developed based on feedback from the focus groups and input from subject matter experts (SMEs). CONCLUSION: This study provides a modified core competency and capability framework comprising 24 competencies grouped under six domains. This study is a step toward international standardization of advanced practice physiotherapy based on a commonly agreed framework for the education and training of advanced practice physiotherapists.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".