Applying the guidelines for pharmacists integrating into primary care teams
Bibliographic record
Abstract
BACKGROUND: In 2013, Jorgenson et al. published guidelines for pharmacists integrating into primary care teams. These guidelines outlined 10 evidence-based recommendations designed to support pharmacists in successfully establishing practices in primary care environments. The aim of this review is to provide a detailed, practical approach to implementing these recommendations in real life, thereby aiding to validate their effectiveness. METHODS: Both authors reviewed the guidelines independently and ranked the importance of each recommendation respective to their practice. Each author then provided feedback for each recommendation regarding the successes and challenges they encountered through implementation. This feedback was then consolidated into agreed upon statements for each recommendation. RESULTS AND DISCUSSION: Focusing on building relationships (with an emphasis on face time) and demonstrating value to both primary care providers and patients were identified as key aspects in developing these new roles. Ensuring that the environment supports the practice, along with strategic positioning within the clinic, improves uptake and can maximize the usefulness of a pharmacist in primary care. Demonstrating consistent and competent clinical and documentation skills builds on the foundation of the other recommendations to allow for the effective provision of clinical pharmacy services. Additional recommendations include developing efficient ways (potentially provider specific) to communicate with primary care providers and addressing potential preconceived notions about the role of the pharmacist in primary care. CONCLUSION: We believe these guidelines hold up to real-life integration and emphatically recommend their use for new and existing primary care pharmacists.
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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 | 0.032 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".