Fostering career-readiness in pharmacy students through work-integrated learning: Qualitative analysis of co-op supervisor and rotation preceptor feedback on student performance
Bibliographic record
Abstract
INTRODUCTION: Work-integrated learning (WIL) is a key component of many professional programs, allowing students to apply classroom knowledge in workplace and practice settings. While most pharmacy schools include clinical rotations in their curriculum, few integrate co-operative education ("co-op"), resulting in a dearth of literature regarding how each WIL model prepares students for pharmacy careers. We analyzed student performance evaluations to identify co-op supervisors' and rotation preceptors' perceptions of students' job/practice readiness skills, distinguishing the unique and complementary skills developed by each experience. METHODS: In the University of Waterloo Doctor of Pharmacy (PharmD) program, students complete three co-op work terms in their second and third years and three clinical rotations in their fourth year. Supervisor and preceptor qualitative feedback on student performance for students in three graduating classes was qualitatively analyzed; two researchers independently coded data, using content analysis to identify themes. RESULTS: Both WIL models support students' growth in confidence, ability to engage in tailored communication with patients, and improved collaboration with other healthcare providers. A hierarchy of learning was observed with co-op helping students gain experience as a contributing member of an interprofessional team and learning how to adapt to workflow changes. This provided a foundation for final-year rotations allowing students to focus and gain self-assurance providing patient care services. DISCUSSION: Supervisors and preceptors perceive that co-op and rotations provide students with multiple important skills for job/practice readiness. Co-op's fostering of job readiness skills prepares students for more advanced, focused, and nuanced practice skill development in the program's final year.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".