Marriage of Individual Pharmacists’ Achievement on Key Performance Indicators and Teaching Responsibilities
Bibliographic record
Abstract
Entry-to-practice Doctor of Pharmacy (PharmD) curricula focus on advanced clinical knowledge and skills, enabling graduates to be competent and confident in providing direct patient care in various practice settings. A significant component of the 4-year program is experiential education, involving both early and advanced pharmacy practice experiences. 1 These programs will require that many more rotations be offered, with greater breadth, quantity, and sophistication of experiences. To create an effective training environment for students and to accommodate the larger number of rotations, some educational programs have indicated that they would like students to be able to “add value” to the practice sites by contributing to patient care. This contribution would offset the increased demand on pharmacist preceptors associated with providing enhanced experiences to a larger number of students. To contribute effectively to patient care to any relevant degree, students will need to assume direct patient care responsibilities. Educational institutions, practice sites, and professional associations have proposed various strategies to facilitate this enhanced training. Models for pharmacy residencies and student training have indicated the importance of both residents and students undertaking patient care activities that are known to improve patient outcomes. 2
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".