The road to collaboration: Developing an interprofessional competency framework
Bibliographic record
Abstract
In the absence of an interprofessional competency framework in Canada, the College of Health Disciplines (CHD) at the University of British Columbia developed a universally applicable framework. This article discusses the development of the "BC Competency Framework for Interprofessional Collaboration". Building on a Health Canada funded initiative through the Interprofessional Network of British Columbia (In-BC), the CHD compared and contrasted existing competency frameworks and consulted curriculum and IP experts throughout British Columbia. The resulting framework is designed to inform curriculum development for health and human service professionals throughout the continuum of learning, starting with pre-licensure education and extending into continuing professional development. The framework will serve as a foundation for future curriculum reform by health and human service educators, practitioners and decision-makers throughout BC and will contribute to the competency literature in 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 | 0.039 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".