Design and implementation of an integrated medication management curriculum in an entry-to-practice doctor of pharmacy program
Bibliographic record
Abstract
Introduction: The design and implementation of the core patient care curriculum (medication management [MM]) in a new Canadian entry-to-practice doctor of pharmacy programme is described. Curriculum Design: The MM curriculum was designed to span the first three years of the programme and comprise 75% of the programme’s coursework. The goal was to achieve multi-disciplinary integration of pharmaceutical and clinical sciences. Seventeen modules were created, within which medical conditions were the main unit of organisation. For each condition, the elements (or themes) most relevant for pharmacists to develop the knowledge and skills necessary for its management were identified. A quarter of curricular time was dedicated to integration activities (IA) created for students to elaborate and integrate their knowledge and demonstrate competency. The curriculum and IA incorporated a spiral progression of complexity and level of performance across year levels, guided by a programme- level cognitive model. Evaluation: Approaches to overcoming challenges identified through pilot-testing, faculty, student, and stakeholder feedback are described.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".