Bridging the Location Gap: Physician Perspectives of Physician - Pharmacist Collaboration in Patient Care (BRIDGE Phase II)
Bibliographic record
Abstract
Background: To optimize patient outcomes, the patient-centred medical home model emphasizes comprehensive team-based care. Pharmacists are qualified to enhance appropriate medication use and help improve patient outcomes through provision of medication therapy management (MTM) services. To optimally provide MTM, pharmacists must effectively collaborate with physicians. This study explored factors that influence pharmacist-physician collaboration.Methods and Findings: A convenience sample of five physicians participated in semi-structured interviews and the resulting data were analyzed using qualitative methods. Transcripts of the interviews were independently coded for themes by two researchers. Five themes emerged: trustworthiness, role specification, relationship initiation, effects on practice, and professional awareness/expectations.Conclusions: Overall interviewees spoke positively about pharmacists; however, when discussing collaboration, they spoke almost exclusively about pharmacists within their clinic. Since most pharmacists practice outside of clinics, bridging the location gap is imperative for collaboration. In addition, physicians lacked an overall understanding of pharmacists’ training and clinical capacity. This may inhibit pharmacists from participating to their full professional capability within integrated healthcare teams. One approach to resolve this lack of physician understanding of pharmacists’ role and value may be to co-educate health professional students. Further research is needed to explore ways to improve interprofessional collaborative care.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".