Shedding More Light on the State of Interprofessional Education
Bibliographic record
Abstract
To the Editor: We are writing in response to the recent article by Drs. Paradis and Whitehead.1 As the University of Toronto’s (UT’s) Centre for Interprofessional Education (CIPE) is referred to as a particular example, we wish to offer a few points of clarification. First, the authors describe the CIPE as “overseeing four core learning activities,” which is not the current state of our curriculum framework. In fact, oversight is provided by a health-sciences-wide Inter-Faculty Curriculum Committee. This strategic arrangement has two key advantages: (1) all involved faculties, regardless of size, have access to an integrated IPE curriculum for their students; and (2) the cost is significantly lower than if all faculties created their own curriculum. In representing IPE at UT, the authors focus on activities offered to large numbers of students in small groups, giving parenthetical acknowledgment to the foundational role of workplace-based learning, which takes shape through clinical placements and practice-based electives. Indeed, clinical IPE placements preceded the IPE curriculum. Our evaluation framework considers the interactions between small-group learning in IPE activities and workplace-based learning, rather than treating each as stand-alone—or parenthetical—activities. Second, the authors operationally define IPE as occurring at the undergraduate level. In putting forward this restricted definition, a more expansive understanding of IPE as a novel contribution to the field is then proposed; however, this is already well established. Indeed, the CIPE supports education for collaboration across the continuum, providing a range of opportunities at undergraduate and postgraduate levels, as well as professional and faculty development programs customized to participants’ workplaces. IPE curriculum is developed with patients and practitioners to reflect practice realities. Further, the CIPE is operationally situated in both education and practice systems as a strategic endeavor between UT and University Health Network, who are joint governors and funders. The authors’ definition of IPE obscures these kinds of institutional relationships and their importance to the field. Finally, we wish to address the authors’ comment regarding the “wizardry” and “event management skills” of those that develop, deliver, support, and evaluate IPE. We acknowledge the logistical demands of a robust IPE program, and we commend the leadership and expertise required to meet such demands. Indeed, much of health professions education (HPE) is reliant on the often-invisible work of those who support any curriculum. IPE is no exception. However, we wish to correct the implication that having the skills required to meet such logistical demands precludes the ability to be either scholarly or rigorous. Such a claim is damaging not just to those who support IPE but to all who support HPE. It risks making invisible the important scholarly work upon which all of HPE depends. At the CIPE and across our broad community, we continue to develop, learn, and innovate collaboratively, and we look forward to further rigorous academic work in the field. Maria Tassone, MScDirector, Centre for Interprofessional Education, University of Toronto, Toronto, Ontario, Canada; [email protected] Dean Lising, MHScStrategy lead, Interprofessional Education Curriculum/Collaborative Practice Lead, Centre for Interprofessional Education, University of Toronto, Toronto, Ontario, Canada. Sylvia Langlois, MScFaculty lead, Interprofessional Education Curriculum and Scholarship, Centre for Interprofessional Education, University of Toronto, Toronto, Ontario, 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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.026 | 0.050 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".