Physiotherapy beyond Our Borders: Investigating Ideal Competencies for Canadian Physiotherapists Working in Resource-Poor Countries
Bibliographic record
Abstract
PURPOSE: To explore the perspectives of Canadian physiotherapists with global health experience on the ideal competencies for Canadian physiotherapists working in resource-poor countries. METHOD: A qualitative interpretive methodology was used, and the Essential Competency Profile for Physiotherapists in Canada, 2009 (ECP), was employed as a starting point for investigation and analysis. Semi-structured one-on-one interviews (60-90 minutes) were conducted with 17 Canadian physiotherapists who have worked in resource-poor countries. Descriptive and thematic analyses were conducted collaboratively. RESULTS: The seven ECP roles-Expert, Communicator, Collaborator, Manager, Advocate, Scholarly Practitioner, and Professional-were all viewed as important for Canadian physiotherapists working in resource-poor countries. Two roles, Communicator and Manager, have additional competencies that participants felt were important. Three novel roles-Global Health Learner, Critical Thinker, and Respectful Guest-were created to describe other competencies related to global health deemed crucial by participants. CONCLUSIONS: This is the first study to examine competencies required by Canadian physiotherapists working in resource-poor countries. In addition to the ECP roles, supplementary competencies are recommended for engagement in resource-poor countries. These findings align with ideas in current global health and international development literature. Future research should examine the relevance of these findings to resource-poor settings within 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".