Implementing and sustaining a mentorship program at a college of pharmacy: The Keys to Successful Mentorship
Bibliographic record
Abstract
OBJECTIVE: To evaluate mentor and mentee opinions of The Keys to Successful Mentorship, a longitudinal student-led mentorship program established at a college of pharmacy. EDUCATIONAL ACTIVITY AND SETTING: In 2008, a mentorship program was created whereby first year pharmacy students (mentees) were paired with third year pharmacy students (mentors). An anonymous survey was administered to second (P2) and fourth (P4) year pharmacy students identifying strengths and weaknesses of the program. FINDINGS: Results of the survey administered to the P2 and P4 pharmacy students revealed that there was a strong desire to take part in the mentorship program. Of the respondents, 77% of P2 and 70% of P4 students stated the mentorship program aided in their professional growth. Mentors disagreed significantly more than mentees that participation in the program should be optional. Qualitative findings suggested that the program assisted students in building professional relationships and networks, better prepared them for experiential training, and helped with post-graduate decisions. CONCLUSION: The implementation of a longitudinal student-led mentorship program was supported by student pharmacists and may aid in their professional development.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".