Competency-Based Education Frameworks Across Canadian Health Professions and Implications for Multisource Feedback.
Bibliographic record
Abstract
BACKGROUND: Feedback in health professional clinical training is typically the responsibility of the student's own supervisor. However, assessment in competency-based education may be optimized by drawing upon the judgments of multiple assessors. Specific interprofessional competencies have been deemed appropriate for multisource feedback, but these skills may not be uniformly described and therefore performance expectations may differ across disciplines. METHODS: We conducted a document content analysis of the educational outcomes for seven Canadian health professional training programs. Competency frameworks for dietetics, medicine, nursing, occupational therapy, pharmacy, physiotherapy, and respiratory therapy were located and systematically compared. RESULTS: All professions organized educational outcomes according to core competencies. As anticipated, interprofessional competencies of communicator, collaborator, and professional appeared in almost all frameworks, but with distinctions in described emphasis and scope. Evidence-based practice is not typically identified as an interprofessional competency but is similarly widely represented across the majority of disciplines. CONCLUSION: Our review suggests common understanding of shared competencies should not be taken for granted insofar as how roles are described across disciplines' educational frameworks. Further study to explore how interprofessional competencies are practically interpreted by clinicians and used to judge students training with their team, but who are outside their own health discipline, is warranted.
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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 | 0.061 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".