An interprofessional practice capability framework focusing on safe, high-quality, client-centred health service.
Bibliographic record
Abstract
This paper describes an interprofessional capability framework which builds on the existing interprofessional competency and capability frameworks from the United Kingdom, Canada, and the United States of America. Existing published frameworks generally make reference to being client-centred and to the safety and quality of care, and locate interprofessional collaborative practice as the central theme or objective. In contrast, this framework interlinks all three elements: client-centred services, safety and quality of services, and interprofessional collaborative practice. The framework is clear and succinct with an accompanying visual representation that highlights all key features. The framework has informed curriculum which incorporates a common first-year, case-based educational workshops and practice placements within a large complex health sciences faculty of approximately 10,000 students from 22 disciplines. The articulation of these key elements of health practice has facilitated students, academic staff, and community health professionals to develop a shared understanding of interprofessional education and practice. The design, implementation, and evaluation of learning outcomes, learning experiences, and assessments have been transformed with the introduction of this framework, which is highly applicable to other contexts.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".