Clinical Instructors’ Perceptions of Internationally Educated Physical Therapists’ Readiness to Practise during Supervised Clinical Internships in a Bridging Programme
Bibliographic record
Abstract
Purpose: The purpose of this study was to describe clinical instructors’ (CIs) comments on the Canadian Physiotherapy Assessment of Clinical Performance (ACP) that reflect areas of strength and areas requiring improvement among internationally educated physical therapists (IEPTs) during supervised clinical internships in a bridging programme. Method: We reviewed the assessment records of 100 IEPTs’ clinical performance during two internships each for three successive cohorts of learners in a Canadian bridging programme. We extracted the CIs’ text from 385 comment sections of the ACP completed during these internships and analyzed them using qualitative content analysis. Results: The iterative deductive coding process resulted in 36 subcategories: 14 for areas of strength and 22 for areas requiring improvement. We merged the 36 subcategories to produce nine categories: four areas of strength (subjective assessment, treatment, patient confidentiality, and professionalism) and five areas for improvement (objective assessment, clinical reasoning, establishment of treatment goals, communication, confidence, and time management). We then grouped these categories into two broad themes: professional practice and professional conduct. Conclusions: The CIs commended the IEPTs for their clinical competence in subjective assessment, treatment, patient confidentiality, and professionalism. The areas requiring improvement typically required more complex clinical decision-making skills, which may have been challenging for these IEPTs to demonstrate as competently during a short internship.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".