Bibliographic record
Abstract
Clinical education is an integral component of physiotherapy student training, 2,3 comprising approximately one-third of all coursework in physiotherapy programmes across Canada.During clinical placements or internships, physiotherapy students develop and apply the knowledge, skills, and professional behaviours necessary for competent entry-level practice, and they are evaluated on these clinical competencies by physiotherapist supervisors or clinical instructors (CIs).At present, most Canadian physiotherapy schools use the Physical Therapist Clinical Performance Instrument (CPI) 4 to assess students' performance during their clinical placements.The CPI consists of 24 items or performance criteria that, together, are considered to represent all aspects of physiotherapy clinical performance.Developed in the United States, the CPI has undergone rigorous development and testing and has been found to be a valid and reliable measure of physiotherapy student performance. 4hile the CPI's psychometric properties have been established, a recent Canadian study 5 identified the CPI and the evaluation of students as a barrier to physiotherapists' offering to supervise a student.The study also confirms anecdotal reports from Canadian CIs that the CPI is lengthy, takes too long to complete, and is not always suited to the Canadian physiotherapy context. 5The new instrument developed by Mori and colleagues 1 is a welcome addition to the evaluation of Canadian physiotherapy students, and I am sure many CIs will say it is long overdue!In an era of evidence-informed practice, and in light of the principles of research we emphasize to the students in our programmes, both the physiotherapy community and our students should expect assessments of student performance to be grounded in evidence.Like the developers of the CPI, Mori and colleagues document a systematic and rigorous process for the initial development of their new instrument, the Canadian Physiotherapy Assessment of Clinical Performance (ACP). 1 In Phase 1, Mori and colleagues consulted widely with experts in assessment and measurement, as well as with experts in Canadian physiotherapy clinical education.Because the ACP was intended to be a national instrument, members of the National Association for Clinical Education in Physiotherapy (NACEP) and the Canadian Council of Physiotherapy Academic Programs (CCPUP) were invited to participate in the Delphi process, ensuring that the developers received feedback and input from all Canadian physiotherapy programmes before reaching consensus on the competencies to be included in the ACP.Phase 2 gathered feedback from academic experts in measurement and clinical education, as well as from end users (i.e., CIs and recent graduates), on the items to be included in the instrument, their understanding of these items, the rating scale to be used, and their overall impressions of the instrument.Cognitive interviewing is an important step in developing surveys and instruments like the ACP because it ensures that the questions or items are understood by the respondent (in this case, the CI or student) as the developers intended, 6 as well as giving potential users an opportunity to provide input on usability.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.056 | 0.049 |
| Insufficient payload (model declined to judge) | 0.021 | 0.022 |
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".