Mapping Course Assessments to Canadian Pharmacy Educational Outcomes to Ensure Pharmacy Students’ Practice Readiness
Bibliographic record
Abstract
Objective. The entry-to-practice PharmD program is designed to meet the Association of Faculties of Pharmacy of Canada (AFPC) Educational Outcomes (EOs). We set out to evaluate how assessment strategies in a ‘capstone’ course align with AFPC EOs, their respective key and enabling competencies, and whether there are a sufficient number of assessments for students to demonstrate achievement of competencies prior to embarking on advanced pharmacy practice experiences. Methods. Each assessment’s objectives, content and methods were mapped to key and enabling competencies of each role. The number of enabling competencies mapped represented the extent to which the associated key competency and broader role was assessed. De-identified student performance data were analyzed to identify achievement of competencies despite failed assessments. Results. Of the seven role descriptions, Care Provider, Communicator, and Collaborator were the most comprehensively assessed. Leader-Manager and Health Advocate roles were assessed to a limited extent. The Scholar role was not covered to a great depth across assessments. The Professional role was not represented in most assessments except for the final exam. Students with failed assessments generally had ample opportunity to demonstrate competencies through other assessments. Conclusion. Mapping assessments to AFPC EOs is an essential step to demonstrate direct evidence of achievement of the intended learning outcomes. Our map revealed that there was sufficient overlap in the assessment of most AFPC EOs with a few exceptions. It is important to create multiple opportunities within a course for students to demonstrate achievement of competencies to ensure practice readiness.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".