An Advanced Pharmacy Practice Experience for Community Pharmacies Based on a Clinical Intervention Targeting Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
Experiential education is a critical component of any pharmacy undergraduate curriculum. Establishing new, high-quality practice sites can be challenging. We designed a new advanced pharmacy practice experiential rotation suitable for implementation in most community pharmacy settings. The aim of this article is to describe the design of this rotation entitled the Targeted Pharmacy Intervention in Inflammatory Bowel Disease (TPI-IBD) and to determine its impact on student knowledge and confidence using a before-after survey design. The TPI-IBD utilizes a student-delivered intervention as a platform for experiential learning in community pharmacy practice. The TPI was focused on patients with IBD, and implementation was guided by a co-preceptor from the university in collaboration with onsite-preceptors at each pharmacy. The TPI-IBD rotation was delivered from 6 community pharmacies during 5 weeks in 2018. Students conducted standardized monitoring on patients with IBD and met weekly with the university preceptor for case presentations and therapeutic discussions. Electronic charts were maintained by students who were responsible for ensuring detailed documentation on each patient. Knowledge, confidence, and overall satisfaction were assessed by a survey given to students before and after the rotation. Students were highly satisfied with the learning experience and improvements in knowledge and confidence were clearly demonstrated. The TPI strategy was an effective way to expand rotation options in community pharmacy sites with minimal burden on local preceptors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".