Advanced musculoskeletal physiotherapy practice: Informing education curricula
Bibliographic record
Abstract
INTRODUCTION: Physiotherapists are operating at an advanced level of practice, usually on ad hoc basis with inhouse training, in response to the increasing burden of musculoskeletal (MSK) disorders. Discrepancies in role-specific education of advanced practice physiotherapists (APPs) creates challenges in ensuring a quality service, workforce mobility and formal recognition. This study reviewed existing MSK APP competency frameworks and education offerings, and explored physiotherapist learning needs with a view to informing international standardisation of MSK APP education curricula. METHODS: A scoping review of the literature and relevant university and regulatory websites identified APP competency frameworks and education curricula, which were verified by international experts. Content analysis, performed on the identified competencies and modules, produced a list of themes existing in MSK advanced practice internationally. A survey based on those themes identified the learning priorities of physiotherapists (n = 25) participating in an APP symposium in Ireland. RESULTS: Six APP competency frameworks and eleven curricula from the UK, Canada and Australia were identified. Themes emerging, regarding MSK APP practice internationally, included both entry-level physiotherapy (e.g., Assessment and Diagnosis) and traditionally medically-controlled tasks (e.g., Injection Therapy), as well as Research, Leadership, Service Development, Professional-related Matters and Education. Participating physiotherapists more commonly prioritised competencies which would be deemed beyond entry level physiotherapy skills (i.e., Radiology versus Manual Therapy). CONCLUSION: Despite variances in profiles of APPs both between and within countries, common themes emerged regarding their expected competencies and skills. This study provides the foundation for the adoption of internationally-recognised MSK APP competencies and education standards.
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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 | 0.045 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".