Pre-clinical Pharmacology Training in a Student-Centered Veterinary Curriculum
Bibliographic record
Abstract
The appropriate use of therapeutics is important to both human and animal health. The field of pharmacology is rapidly progressing such that it is impossible to convey to students every possible piece of information they will need to know throughout their veterinary careers. Instead, it is more important to train students for lifelong and self-directed learning so that they will be able to adapt to the ever-changing pharmaceutical landscape. Western University of Health Sciences College of Veterinary Medicine teaches pharmacology using a student-centered and problem-based curriculum designed to teach students not only the basics of pharmacology and clinical pharmacology, but also the personal skills needed to continue to learn beyond their formal education. The aim of this manuscript is to document the pharmacology curriculum during phase I of the veterinary curriculum. Review of the graduating class of 2010's exposure to pharmacology learning issues reveals broad-based coverage of major functional and mechanistic drug classes as well as peripheral topics, including pharmacokinetics, legal and ethical issues, and dosing regimen calculations. Previous classes have scored well on external examinations leading to a belief that this pharmacology curriculum provides adequate training for graduate veterinarians.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".