iEthics: An Interprofessional Ethics Curriculum
Bibliographic record
Abstract
This article discusses the development, content, implementation, and evaluation of an interprofessional ethics curriculum that has been integrated as a required component of learning in the Faculty of Pharmaceutical Sciences at the University of British Columbia (UBC), along with 12 other health professional programs. We start by giving a background and rationale for the development of the integrated ethics (iEthics) curriculum, led by UBC Health, and provide an overview of the pedagogical approach used, curriculum model, and content. We outline the way in which the iEthics curriculum has been implemented in the Faculty and share findings from program evaluations. In the discussion section, we reflect on our experience as facilitators for the interprofessional workshops and link these experiences with the findings from the program evaluations. These reflections highlight the way in which the iEthics curriculum has been successful in meeting the desired outcomes of learning in terms of the interprofessional delivery, and provide insights into how the findings from the iEthics evaluation informed other modules in the integrated curriculum and its implementation in the Faculty of Pharmaceutical Sciences.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".