Assessment approaches and methods in physiotherapy education: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Assessment in physiotherapy education is essential to ensuring graduates meet professional standards, demonstrate competence and are prepared to provide high-quality, patient-centered care. Assessment approaches (principles guiding assessment design, sequence and implementation) and methods (techniques used to evaluate performance) should be constructively aligned with curriculum content and learning outcomes, and assess the breadth of competencies required for practice. While individual studies describe assessment approaches and methods in physiotherapy education, there is no comprehensive synthesis or strategic analysis of their strengths, weaknesses, opportunities and threats. OBJECTIVES: To 1) identify and characterize assessment approaches and methods used in physiotherapy education; 2) analyze the strengths, weaknesses, opportunities and threats of assessment approaches and methods used in physiotherapy education. METHODS: This scoping review will follow Joanna Briggs Institute guidance and report according to Preferred Reporting Items for Systematic review and Meta-analysis extensions for Protocols and Scoping Reviews. The protocol is registered with Open Science Framework. Searches will be performed in Medline, CINAHL, EMBASE, ProQuest Education Research Index, Scopus, PsycINFO, and relevant grey literature from inception until July 23, 2025. Eligible studies will report primary research on assessment approaches or methods in the academic curriculum of university-based clinical physiotherapy education programs. Studies focusing only on clinical education, reviews, opinion pieces will be excluded. Two reviewers will independently screen studies and extract data using a standardized tool. A narrative synthesis will characterize assessment approaches and methods, categorizing approaches by guiding principles and methods by type. Methods will be mapped to Miller's pyramid of clinical competence. A directed content analysis will identify the strengths, weaknesses, opportunities and threats influencing assessments in physiotherapy education. CONCLUSION: This review will provide a comprehensive synthesis and strategic analysis of assessments in physiotherapy education. Findings will inform evidence-based assessment practices that support optimal student learning, experiences and readiness for contemporary practice.
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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 | 0.197 | 0.170 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.027 |
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".