Medicinal chemistry curriculum and pedagogical practices at Canadian pharmacy schools: Towards standardization of practice
Bibliographic record
Abstract
INTRODUCTION: Medicinal chemistry instruction in PharmD programs at Canadian universities is considered an important foundational science. However, with few guidelines for the required content most programs have observed a decrease in hours of medicinal chemistry instruction. A Medicinal Chemistry Special Interest Group (SIG) was formed to address these issues nationally and initiated a pan-Canadian environmental scan to better understand the depth and breadth of medicinal chemistry instruction. METHODS: The SIG carried out an environmental scan to identify medicinal chemistry content, delivery and assessments in PharmD programs in Canada. RESULTS: Core medicinal chemistry concepts across the PharmD programs are in general agreement with those listed by the Accreditation Council for Pharmacy Education. Medicinal chemistry was typically taught as didactic lectures either as a standalone course or within a pharmacology course, although one program integrated some medicinal chemistry within therapeutics focused problem-based learning. There was no consistent time in program where medicinal chemistry occurred. CONCLUSIONS: The SIG found that similar medicinal chemistry content is taught across all Canadian PharmD programs, but incorporation of medicinal chemistry in therapeutics courses was minimal. Core concepts within six high-level overarching themes that guide our collective instruction were identified. The core concepts require developing high-level cognitive processes such as knowledge application and synthesis that practicing pharmacists are expected to possess for entry to practice. We the authors posit that in addition to providing a unique tool for pharmacists to employ in therapeutic decision-making, medicinal chemistry also provides early practice of important problem-solving and critical thinking skills.
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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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".