Clinical Performance Scores of Internationally Educated Physical Therapists during Clinical Education in a Bridging Programme
Bibliographic record
Abstract
Purpose: We describe the numerical ratings assigned by clinical instructors to the performance of internationally educated physical therapists (IEPTs) during their clinical internships while enrolled in a bridging programme. Method: We conducted a secondary analysis of the quantitative data for IEPT learners attending the Ontario Internationally Educated Physical Therapist Bridging Program using the Canadian Physiotherapy Assessment of Clinical Performance (ACP) tool. We extracted the scores from each IEPT’s ACP form at the midterm and final evaluations for two internships and conducted a descriptive analysis. Results: We obtained 318 data sets for 61 IEPTs. By the final point of the second internship, (1) items about communication pertaining to ethical professional relationships, conducting oneself within legal and ethical requirements, and respecting the individuality and autonomy of the client had high mean ratings; (2) most items rated achieved advanced intermediate performance and many indicated entry-level performance; and (3) most IEPTs (84%) either had high scores throughout or improved from lower scores to at least advanced intermediate performance. Conclusions: Items relating to professional conduct and effective communication in professional relationships were relatively high among the IEPTs. By the end of the second internship, most IEPTs in this bridging programme had improved their clinical performance toward or up to the entry-level standard for Canadian physiotherapists.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".