Enhanced Patient-Centred Care: Physiotherapists' Perspectives on the Impact of International Clinical Internships on Canadian Practice
Bibliographic record
Abstract
PURPOSE: To explore the perspectives of physiotherapists who participated in an international clinical internship (ICI) in low- or middle-income countries (LMICs) during their physiotherapy (PT) training in a Canadian PT programme regarding the ICI's impact on their PT practice in Canada. METHODS: This qualitative descriptive study used in-depth semi-structured interviews. Data were organized using NVivo; inductive and deductive coding were used to analyze data and develop broader themes. RESULTS: The 13 practising Canadian physiotherapists interviewed described three enhanced capacities: (1) critical reflection on culture, values and practice; (2) communication skills; and (3) creativity and resourcefulness. These capacities were perceived to transfer to Canadian practice by enhancing participants' ability to deliver patient-centred care, specifically through an enhanced understanding of patients' values and social determinants of health, regardless of the Canadian setting or patient population. CONCLUSIONS: For PT students considering an ICI, the study findings provide insight into the perceived impact of ICIs on Canadian practice. For PT academic programmes, the findings can guide decisions on the extent of investment in ICIs as learning opportunities that will enhance practice in Canada.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".