The Association between Academic Performance and Entry-to-Practice Milestones within a Co-Operative Education PharmD Program
Bibliographic record
Abstract
Research on associations between student performance in pharmacy programs and entry-to-practice milestones has been limited in Canada and in programs using a co-operative (co-op) education model. Co-op exposes students to a variety of opportunities both within direct patient care roles and in non-traditional roles for pharmacists, such as policy, advocacy, insurance, research, and the pharmaceutical industry. The purpose of this research is to analyze associations between student grades and evaluations achieved in the University of Waterloo (UW) Doctor of Pharmacy (PharmD) co-op program and success rates on entry-to-practice milestones, including the Pharmacy Examining Board of Canada (PEBC) Pharmacist Qualifying Examination and performance on final-year clinical rotations. Grades and evaluations from courses, co-op work terms, clinical rotations, and PEBC exam data from three graduating cohorts were obtained. A multiple regression analysis was performed to explore associations between student evaluations and PEBC Pharmacist Qualifying Examination and clinical rotation performance. Holding all other variables constant, grades in anatomy/physiology were negatively correlated with scores on the PEBC Pharmacist Qualifying Examination, while grades in one of the professional practice courses showed a positive relationship with the same examination. Students with higher grades in a problem-based learning capstone therapeutics course, in their first co-op work term, and in the direct patient care co-op work term tended to score higher on clinical rotations. Co-op performance was not significant in predicting PEBC performance. However, complimentary descriptive analysis underscored that students with a co-op rating of good or below were more likely to fail courses, midpoint evaluations, Objective Structured Clinical Examinations (OSCEs), and PEBC measures. Multiple predictors of performance on final-year clinical rotations and the PEBC Pharmacist Qualifying Examination were identified. This predictive model may be utilized to identify students at risk of underperforming and to facilitate early intervention and remediation programs, while also informing curricular revision.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".