Implementing pain competencies in Canadian physiotherapy education: Challenges, barriers, and opportunities
Bibliographic record
Abstract
Background: The Pain Education in Physiotherapy (PEP) competency profile provides a structured framework for integrating pain management competencies into Canadian physiotherapy (PT) curricula. Despite widespread endorsement, the integration of pain management competencies into PT curricula remains inconsistent. Identifying the barriers and enablers to implementation is essential for developing strategies that support students in achieving these competencies. Objective: This study explored factors influencing the implementation of the PEP competency profile in Canadian PT programs and identified key challenges and opportunities for improving integration. Methods: A qualitative description study was conducted using five focus groups with 23 participants, including pain educators and program directors from 13 entry-level PT programs in Canada. Data were analyzed using the Consolidated Framework for Implementation Research to identify multilevel barriers and facilitators. Results: Participants recognized the value of the PEP competency profile in enhancing pain education but highlighted three key challenges: (1) a lack of structured guidance for teaching and assessment, (2) an overreliance on faculty champions rather than systemic institutional support, and (3) the absence of rigorous assessment approaches. Participants expressed uncertainty about integrating competencies within existing curricula, emphasizing the need for national collaboration, faculty development, and shared resources. The iterative, decentralized nature of curriculum change further complicated efforts to achieve consistent integration. Conclusion: Sustainable implementation of the PEP competencies requires structured guidance, institutional commitment, and adapted assessment strategies. Addressing these barriers through national-level collaboration, accreditation alignment, and faculty support is critical to ensure that PT graduates develop the necessary competencies for high-quality pain management.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".