Teaching and Assessing Advocacy in Canadian Physiotherapy Programmes
Bibliographic record
Abstract
Purpose: Advocacy is an essential component of physiotherapy (PT) practice. As a result, universities are expected to teach and assess advocacy-related competencies in their curriculum. The purpose of this study was to explore current educational practices for teaching and assessing advocacy in Canadian PT programmes, barriers to teaching and assessment, and solutions for enhancing educational practices. Method: We used a convergent parallel mixed-methods design. Teachers and coordinators from Canadian PT programmes completed an online survey, and clinical supervisors participated in telephone interviews. We performed descriptive statistics and thematic analyses. Results: Advocacy-related competencies were widely covered in the academic curriculum of the 13 PT programmes represented by our participants, but not all competencies were assessed equally. Barriers to teaching and assessment of advocacy included the lack of role clarity, relevant teaching and assessment strategies, time, and opportunity to practice the role in the curriculum. Students’ personal experience and motivation also had an impact. Conclusion: Essential steps toward enhancing educational practices are to clarify the definition of advocacy, guide PT educators in explicitly and concretely teaching and assessing advocacy, develop a staged approach to covering advocacy throughout the curriculum, and normalize advocacy as a PT domain.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".