Identifying the core concepts of pharmacology education: A global initiative
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In recent decades, a focus on the most critical and fundamental concepts has proven highly advantageous to students and educators in many science disciplines. Pharmacology, unlike microbiology, biochemistry, or physiology, lacks a consensus list of such core concepts. EXPERIMENTAL APPROACH: We sought to develop a research-based, globally relevant list of core concepts that all students completing a foundational pharmacology course should master. This two-part project consisted of exploratory and refinement phases. The exploratory phase involved empirical data mining of the introductory sections of five key textbooks, in parallel with an online survey of over 200 pharmacology educators from 17 countries across six continents. The refinement phase involved three Delphi rounds involving 24 experts from 15 countries across six continents. KEY RESULTS: The exploratory phase resulted in a consolidated list of 74 candidate core concepts. In the refinement phase, the expert group produced a consensus list of 25 core concepts of pharmacology. CONCLUSION AND IMPLICATIONS: This list will allow pharmacology educators everywhere to focus their efforts on the conceptual knowledge perceived to matter most by experts within the discipline. Next steps for this project include defining and unpacking each core concept and developing resources to help pharmacology educators globally teach and assess these concepts within their educational contexts.
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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 | 0.090 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".