Performance Patterns Among Internationally Educated Physical Therapist During Clinical Education in a Bridging Program
Bibliographic record
Abstract
Background: The Ontario Internationally Educated Physical Therapy Bridging (OIEPB) program was designed to help internationally educated physiotherapists (IEPTs) increase their success at entering practice in Canada. The learners (IEPTs) in the OIEPB program undertake two clinical internships, at which they are assessed by preceptors using the Canadian Physiotherapy Assessment of Clinical Performance (ACP). Our overall goal was to understand the professional competencies and deficiencies in IEPTs as they attempt to integrate into the Canadian physiotherapist workforce. Methods: Phase 1 was a review of literature to explore the experiences of internationally educated healthcare professionals in their new country; data were analyzed using a conventional content analysis, mapped to the professional competency roles. Phases 2 and 3 were secondary analyses of the data in learners' ACP forms. Phase 2: we descriptively analyzed the scores in the ACP forms. Phase 3: we employed deductive direct content analysis to describe the content of the comment sections of the ACP forms. Results: For phase 1, 13 studies were included. Communicator role was the most frequently discussed role. Cultural-language and confidence deficits contributed to deficiencies in roles and competencies among IEHPs. For phase 2, most of the 61 learners' scores indicated entry-level performance for 18 of 21 ACP items at the end of both internships. Most IEPTs (84%) either had high scores throughout or had improvements from lower scores to at least 'advanced intermediate' performance level at the end of the second internship. For phase 3, 100 learners’ comment sections were analyzed. Professional conduct and professional practice were the two central themes, with 11 categories for areas of strength (4) and areas of weakness (7). Conclusion: Most IEPTs showed a progressive improvement in their clinical education performance across internships as judged by ACP scores. Areas of strength included subjective assessment, treatment delivery, patient confidentiality, and inter-professional respect. Areas of weakness included objective assessment, setting SMART goals, logical clinical reasoning, communication, confidence, time management and patient safety. The findings about IEPTs in the OIEPB program are partially congruent with the literature in showing the importance of cultural-language competency and confidence in fulfilling professional roles.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".