Roadmap and Reflections on Expanding Equity, Diversity, Inclusion, Indigeneity, and Accessibility in Pharmacology Curricula
Bibliographic record
Abstract
Nurturing learning systems that are respectful and welcoming to diverse individuals is a step towards improving the experience of all students in pharmacology and toxicology (Pharm-Tox). This paper evaluates the Pharm-Tox curriculum at the University of Toronto with a critical lens towards the incorporation of content that is Equitable, Diverse, Inclusive, acknowledges Indigeneity, and is Accessible (EDIIA). A curriculum mapping approach examined the undergraduate Pharm-Tox curriculum to identify EDIIA gaps and areas for improvement. Key stakeholders that contributed to the curriculum mapping process were undergraduate students, teaching faculty, and an external research associate who identified EDIIA themes used to evaluate existing course materials. The curriculum map identified areas to improve EDIIA integration in individual courses and resulted in the design of course-specific recommendations. Centrally housed department resources were also developed to mitigate barriers to faculty implementation of the EDIIA recommendations. These resources included an internal EDIIA handbook on appropriate language in the classroom, a guide to creating accessible and inclusive PowerPoint slides, and a pre-course survey to identify the student population and their needs. Resources were well received by faculty, and to assess the impact of EDIIA recommendations on student learning, ongoing review of curricular changes will be conducted through student surveys. The recommendations from this curriculum mapping process encourage faculty to explore opportunities for EDIIA integration in the undergraduate Pharm-Tox curriculum with the goal of strengthening the existing curriculum and improving the student learning experience.
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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 | 0.050 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".