Prioritizing competencies for interprofessional education: Expert insights for local and institutional implementation
Bibliographic record
Abstract
Abstract Introduction Interprofessional education (IPE) is essential for fostering collaboration among health care professionals, yet its implementation in academic settings faces significant challenges. To facilitate stakeholder engagement in adapting a competency framework at the institutional level, we used Barr's categorization model to prioritize key collaborative competencies. This study explored expert perspectives on competency selection to inform the development of an IPE curriculum tailored to institutional needs. Method A modified Delphi method was employed to collect input from a diverse panel of 26 health care professionals with expertise in teaching, precepting, and curriculum development. Panel members prioritized 40 competencies from the Canadian Interprofessional Health Collaborative framework, identifying those that should be emphasized as core collaborative competencies for guiding the development of local IPE curricula. The process unfolded over three rounds, allowing experts to refine their responses based on group feedback. Results The study identified 26 core competencies for inclusion in the IPE curriculum. Despite a high consensus rate, comments and narrative feedback highlighted the importance of ensuring foundational knowledge is developed in uniprofessional programs before transitioning to interprofessional settings. Experts emphasized the need for holistic competency frameworks and noted challenges in sequencing, teachability, and assessment, particularly for competencies requiring experiential learning, such as conflict resolution and trust‐building. Conclusion This paper outlines the development of a shared institutional competency framework, emphasizing instructors' priorities and concerns in creating an IPE curriculum across health‐related programs at a bilingual university. The findings suggest that Barr's competency categorization is most effective when applied holistically, as isolating core IPE competencies can lead to confusion and misalignment with uniprofessional curricula. Moving forward, institutional support and active engagement with workplace learning stakeholders will be essential for successful implementation and long‐term sustainability.
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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 | 0.071 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".