Interprofessional Education and Collaborative Competency Development: A Realist Evaluation
Bibliographic record
Abstract
Collaboration among healthcare professionals has been widely cited as critical in ensuring optimal and efficient client care. To foster the development of this interprofessional competency in healthcare graduates, the University of Toronto created an Interprofessional Education (IPE) curriculum. However, the means by which the IPE curriculum developed interprofessional collaborative competencies in occupational therapy (OT) graduates had not been explored. The study identified the mechanisms and outcomes of University of Toronto’s IPE curriculum that contributed to OT graduates’ collaborative competency development. This study also identified the contexts in which this development occurred, and why such patterns were observed. This study employed a mixed-methods realist evaluation, which is an approach underpinned by program theories hypothesizing that specific contexts and mechanisms result in distinct outcomes. Qualitative and quantitative data from 2018 and 2019 OT graduates’ surveys, assessments, interviews, and reflection papers were utilized to test and refine initial program theories. Analysis revealed six outcomes that contributed to interprofessional collaboration: role clarification, team functioning, interprofessional communication, interprofessional conflict resolution, collaborative leadership, and advocacy. The analysis identified mechanisms that enabled and disabled the development of each outcome, and tested initial program theories, which aided refinement. The findings of this study can inform IPE curricula development, promote collaborative competency development in future OT graduates, and direct future IPE evaluation research.
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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 | 0.081 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".