A systematic integrated technique for developing subject matter competencies in pharmacy education
Bibliographic record
Abstract
Pharmacy educators delivering any component of the curriculum face a fundamental question—‘what should I teach?’. As the pharmacist’s scope expands and healthcare evolves, content increases, while curriculum time generally remains static. To optimize limited contact time, educators need clear guidance on essential content for safe and effective professional practice. While accreditation bodies provide general guidance for curriculum structure, individual programs and instructors have the discretion to determine subject matter content delivered to fulfill broader curriculum objectives. For example, the Association of Faculties of Pharmacy of Canada (AFPC) Educational Outcomes are described as a ‘framework for curriculum design without being overly prescriptive’ [1]. While this approach gives programs the freedom to tailor content to suit their structure, it provides little direction for educators delivering various facets of the curriculum. To guide educators and curriculum designers, subject matter competency frameworks can be developed for incorporation into the broader curriculum. Competency-based education has been widely adopted in many health professions, with potential benefits described for pharmacy education [2]. While terminology surrounding educational outcomes differs between regions, for our purposes, competencies are ‘observable abilities of a pharmacist, integrating multiple components such as knowledge, skills, values, and attitudes, and expressed as actual behaviour’ [3]. An example of how competencies can be used in a framework for curriculum design is seen in the AFPC Educational Outcomes [1].
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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 | 0.069 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".