Sustainable Implementation of Interprofessional Education Using an Adoption Model Framework
Bibliographic record
Abstract
Interprofessional education (IPE) is a growing focus for educators in health professional academic programs. Recommendations to successfully implement IPE are emerging in the literature, but there remains a dearth of evidence informing the bigger challenges of sustainability and scalability. Transformation to interprofessional education for collaborative person-centred practice (IECPCP) is complex and requires “harmonization of motivations” within and between academia, governments, healthcare delivery sectors, and consumers. The main lesson learned at the University of Manitoba was the value of using a formal implementation framework to guide its work. This framework identifies key factors that must be addressed at the micro, meso, and macro levels and emphasizes that interventions occurring only at any single level will likely not lead to sustainable change. This paper describes lessons learned when using the framework and offers recommendations to support other institutions in their efforts to enable the roll out and integration of IECPCP.
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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 | 0.030 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".