Development of the Canadian Physiotherapy Assessment of Clinical Performance: A New Tool to Assess Physiotherapy Students' Performance in Clinical Education
Bibliographic record
Abstract
PURPOSE: To develop the first draft of a Canadian tool to assess physiotherapy (PT) students' performance in clinical education (CE). Phase 1: to gain consensus on the items within the new tool, the number and placement of the comment boxes, and the rating scale; Phase 2: to explore the face and content validity of the draft tool. METHODS: Phase 1 used the Delphi method; Phase 2 used cognitive interviewing methods with recent graduates and clinical instructors (CIs) and detailed interviews with clinical education and measurement experts. RESULTS: Consensus was reached on the first draft of the new tool by round 3 of the Delphi process, which was completed by 21 participants. Interviews were completed with 13 CIs, 6 recent graduates, and 7 experts. Recent graduates and CIs were able to interpret the tool accurately, felt they could apply it to a recent CE experience, and provided suggestions to improve the draft. Experts provided salient advice. CONCLUSIONS: The first draft of a new tool to assess PT students in CE, the Canadian Physiotherapy Assessment of Clinical Performance (ACP), was developed and will undergo further development and testing, including national consultation with stakeholders. Data from Phase 2 will contribute to developing an online education module for CIs and students.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".