Scoring of the Physical Therapist Clinical Performance Instrument (PT-CPI): Analysis of 7 Years of Use
Bibliographic record
Abstract
PURPOSE: The aims of this study were to (1) describe the completion rates of the 24 performance criteria (PCs) from the Physical Therapist Clinical Performance Instrument (PT-CPI) by clinical instructors; (2) evaluate change in PC visual analogue scores (VAS) with students' clinical experience; and (3) evaluate scoring patterns over time. METHODS: Final VAS scores for 208 physiotherapy (PT) students (seven cohorts) from 1,039 clinical placements between 2001 and 2008 were analyzed. Completion rates were calculated for each PC. Kruskal-Wallis tests evaluated differences in VAS scores between cohorts. Friedman's tests were used to compare VAS scores for each PC over time. RESULTS: Completion rates were above 90% for 18 PCs. Data from the seven cohorts were combined. All PC scores showed significant change from 10 to 15 weeks and from 15 to 20 weeks of clinical experience (p≤0.001). Although differences in scores decreased over time, 19 PCs showed significant differences between 20 and 25 weeks, and 11 PCs showed significant differences between 25 and 31 weeks of clinical experience (p<0.01). CONCLUSIONS: Certain PCs had lower completion rates. The PT-CPI was used consistently by clinical instructors to evaluate student performance over time. A continual progression in acquisition of clinical competencies was illustrated by PT-CPI scoring patterns as students advanced through their PT education programme.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".