The educational pathway to Advanced Practice for the physiotherapist: A systematic mixed studies review
Bibliographic record
Abstract
BACKGROUND: Advanced Practice Physiotherapy (APP) is a higher level of practice grounded in 4 pillars: clinical practice, leadership, education and research. A critical step toward successful integration and sustainability of APP in healthcare systems is understanding the educational pathway to APP. OBJECTIVES: 1) To describe the post-licensure educational pathways that physiotherapists engage in to advance their level of practice. 2) To evaluate demonstration of the pillars of APP by the physiotherapist after traversing a post-licensure educational pathway. METHODS: This systematic mixed studies review is reported in accordance with PRISMA and pre-registered (PROSPERO: CRD42024499563). 8 databases plus the grey literature were searched. 2 independent reviewers determined eligibility, extracted data, assessed quality (QuADS) and determined the overall confidence in the cumulative evidence (GRADE-CERQual). RESULTS: 81 studies (18 qualitative, 17 mixed methods, 46 quantitative) were included in a data based convergent qualitative synthesis. 6 distinct post-licensure educational pathways were described and evaluated: Masters level education, residency and fellowship programs, accredited area of practice education, mentorship, multiple encounter courses and single encounter courses. CONCLUSION: There is a high level of confidence (GRADE-CERQual) in the finding that Masters level education consistently resulted in all 4 pillars demonstrated by the physiotherapist. Masters level education appears to be the optimal pathway to APP.
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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 | 0.049 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".