Assessment of Student-Reported Preparedness in Leadership and Professional Service Management Post Capstone Course
Bibliographic record
Abstract
OBJECTIVE: Our objective was to explore the effectiveness and utility of a newly introduced capstone course for developing leadership and management skills in pharmacy students. METHODS: A secondary analysis was conducted on precourse and postcourse questionnaire data collected from third-year doctoral-level students and their mentors. The frequency and mean scores of responses to statements regarding confidence in leadership, management, and collaboration skills scored on the Likert scale were presented. Open-ended comments from students and mentors were also grouped and summarized. RESULTS: For all questionnaire items related to the confidence self-assessment, the majority of students responded either agree (40%-69% per question) or strongly agree (6%-56% per question). Students also showed a significant increase in confidence in their management skills after completing the course. Student comments and feedback regarding the course were grouped into 3 major categories: content, timeline/organization, and group dynamics. CONCLUSION: Based on feedback from students and mentors, the capstone course is a valuable learning experience for pharmacy students and an effective tool for developing leadership, management, and collaboration skills. The value of in-person learning, engaged mentors, and real-world relevance for the capstone project is highlighted.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".