Integrating Social Work Into Interprofessional Education
Bibliographic record
Abstract
The University of Toronto Interprofessional Education Curriculum (IPE) is an exemplar of advancing interprofessional education with a focus on preparing students for practice in healthcare settings. Our paper begins with a detailed overview of the University of Toronto’s IPE program including the range of participating faculties, an overview of the curriculum including examples of learning activities, and the social work specific expectations that are embedded in the core and elective components. Following, is a discussion on mitigating the challenges and engaging opportunities associated with integrating social work in a healthcare-focused IPE program at a major Canadian University. Our exploration of mitigating challenges and engaging opportunities will span five key areas: a) Creating meaningful learning experiences for social work students; b) Implementing mandatory or elective IPE participation; c) Scheduling of IPE activities; d) The role of social work faculty in driving student involvement in IPE; and e) Strengthening social work professional leadership for IPE.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".