Assessing Physical Therapy Students' Performance during Clinical Practice
Bibliographic record
Abstract
PURPOSE: To assess the feasibility and acceptability of using the Assessment of Physiotherapy Practice (APP) instrument to assess physiotherapy students' clinical competencies. METHODS: A convenience sample of clinical educators (CEs) and students from the University of British Columbia (UBC) in Vancouver, Canada, completed the instrument currently in use, the Physical Therapist Clinical Performance Instrument (PT-CPI), and the APP. A cross-sectional survey of CEs and physiotherapy students was conducted from 2011 to 2012; the survey included questions worded to elicit opinions about the two instruments when used in the clinical environment with students at different stages of training. Questions addressed various aspects of the instruments, including ease of use, provision of feedback, and completion time. RESULTS: Data were analyzed from 63 CEs from a variety of settings; sufficient data were recorded on 71 student PT-CPI and APP forms. A grading comparison between the PT-CPI and the APP demonstrated equivalence of entry-to-practice standard. Mean completion time was 80 (SD 53) minutes for the PT-CPI and 23 (13) minutes for the APP; mean time difference was 57 (95% CI, 39-75). Students would prefer (82%) that the APP be used to provide feedback and assess their performance on clinical placements. CONCLUSIONS: It is feasible and acceptable to use the APP to assess physiotherapy students' clinical competencies at the University of British Columbia.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".