Exploring Pharmacy Trainee Experiential Learning in a Full Scope Collaborative Rural Primary Care Practice: A Retrospective Chart Review
Bibliographic record
Abstract
Despite reported benefits of pharmacy trainees (e.g., pharmacy students, pharmacy residents) in hospital settings, limited research on the impact of these trainees has been conducted in rural primary care. To explore the potential benefits and impact of pharmacy trainees practicing in a supervised collaborative rural primary care setting, a retrospective chart review was conducted. Drug therapy problems (DTPs) were classified using the Pharmaceutical Care Network Europe (PCNE V9) system. Valuation was measured using a validated tool developed by Overhage and Lukes (1999). Over 16 weeks on a part-time basis, pharmacy trainees (n = 3) identified 366 DTPs during 153 patient encounters. The most common causes for DTPs were related to patient transfers and the need for education. Drug level interventions carried out directly by trainees under supervision accounted for 13.1% of total interventions. Interventions that required prescriber authorization had an acceptance rate of 83.25%, 25% higher than previous acceptance rates found in urban primary care settings. About half (51%) of the interventions proposed and made by pharmacy trainees were classified as significant or very significant, suggesting these trainees added significant value to the pharmacy service provided to rural community residents. This study suggests that pharmacy trainees can be effective resources and contribute meaningfully to patient care in a collaborative rural primary care team setting.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".