Exposing Pharmacy Residents to Implementation Science
Bibliographic record
Abstract
The American Society of Health-System Pharmacists (ASHP) aims to improve patient care by innovating pharmacy practices. ASHP-accredited pharmacy residencies require projects that enhance pharmacy practice, focusing on effective project management and quality improvement. However, only a few of these innovations smoothly become part of routine clinical practice. One solution worth exploring involves teaching Implementation Science in residencies. Exposing residents and mentors to Implementation Science offers two main benefits. First, it helps learn from failed interventions by considering alternative thoughts and grasping environmental influences, leading to smarter decisions in future implementations. Second, applying implementation science improves patient care by turning evidence-based practices into practical actions, ensuring better care, consistency across healthcare setups, fewer errors, and tailoring innovative services to specific institutional needs. Exposing pharmacy residents to implementation science pushes forward pharmacy practice by actively applying evidence-based innovations in broader pharmacy or clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".