Interprofessional collaboration in pharmacist-led primary care clinics
Bibliographic record
Abstract
Background: Pharmacists are essential to team-based care, contributing knowledge and expertise that positively impact patient care. However, it is less clear how interprofessional collaboration is enacted by pharmacists in single-disciplinary outpatient practice environments. Methods: We recruited pharmacists from 3 university-affiliated, pharmacist-led primary care clinics in Canada. Employing a social network analysis approach, the data collection encounter explored the nature and composition of collaboration in patient care. We conducted semistructured interviews, including participant drawings (sociograms), to illustrate their network experiences caring for specific patients. Transcripts from the data collection encounters were systematically coded and analyzed in an iterative process. Initial codes were generated inductively and broader categories refined through constant comparison and finalized by author discussion. Results: Eleven (78%) pharmacists were interviewed and drew 28 sociograms. Pharmacist networks encompassed a wide array of health professionals and patient family members. Despite the physical distribution of interprofessional members, pharmacists formed and maintained relationships to support patient care through conscientious communication and medication management decision-making. Network relationships and system factors influenced collaboration and patient care, often resulting in practice paralysis and/or the need to re-emphasize patient self-advocacy. Interpretation and conclusion: Our findings underscore the dynamic nature of pharmacist networks and how they are navigated to support pharmacist-led medication management. Primary care pharmacists attempt to overcome encountered barriers to implementing patient care plans through various strategies, including leveraging new and existing network relationships. System obstacles impeding effective and efficient patient care could in part be overcome through pharmacist scope of practice expansion.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".