Exploring Physiotherapy Students’ Competencies in Clinical Setting Around the World: A Scoping Review
Bibliographic record
Abstract
Clinical education is a fundamental component of physiotherapy entry-level education programs, with practice education being a core element. The aim of this scoping review is to identify and define the competencies of physiotherapy students in clinical settings. This review followed the five steps of the Arksey and O’Malley framework, employing a search strategy that utilized Mesh terms. Searches were performed in PubMed, CINAHL, Scopus, ERIC, WOS, and PsycINFO, with additional exploration of the grey literature. The inclusion criteria were defined using Peter’s concept: (population) physiotherapy students; (concept) competencies aligned with the WCPT educational framework; (context) clinical environment. A total of 1798 sources were identified, and 86 studies were finally selected. Studies originated from 14 countries, with the United States, Australia, and Canada being the most represented. Of the selected studies, 62 used specific tools to identify competencies. The “Clinical Performance Instrument” and the “Assessment of Physiotherapy Practice” were the most frequently used instruments and covered all of the World Physiotherapy competencies’ domains. “Clinical Competence” emerged as the most widely recognized and utilized term across countries. Among the WCPT domains, “Physiotherapy Assessment and Intervention”, “Ethical and Professional Practice”, and “Communication” were the most represented, whereas “Evidence-Based Practice” and “Quality Improvement” were the least mentioned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".