What Clinical Instructors Want: Perspectives on a New Assessment Tool for Students in the Clinical Environment
Bibliographic record
Abstract
PURPOSE: Many Canadian physical therapy education programs use the 1997 version of the Physical Therapist Clinical Performance Instrument (PT-CPI) to evaluate students in their clinical placements. Recent evidence that clinical instructors (CIs) are unsatisfied with the PT-CPI, however, suggests a need to develop a new assessment tool. The purpose of this study was to gather Canadian CIs' perspectives on rating scales, preferred training methods, and format for future tool development. METHODS: This qualitative descriptive study involved five focus groups from across Canada. English-speaking CIs who had supervised at least one Canadian student in clinical practice were eligible for the study. RESULTS: Participants identified concerns with the PT-CPI and indicated a preference for (1) more objective rating scales with clearly defined anchors, (2) both in-person and online training methods for CIs, and (3) a tool that could be completed and reviewed on paper or online. CONCLUSIONS: CIs affirmed the need to develop a new assessment tool. RESULTS of the study will be used to inform the development of a new assessment tool to better evaluate Canadian physical therapy students' performance in the clinical setting.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".