Integrating pharmacists into primary care teams: barriers and facilitators
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the barriers and facilitators that were experienced as pharmacists were integrated into 23 existing primary care teams located in urban and rural communities in Saskatchewan, Canada. METHODS: Qualitative design using data from one-on-one telephone interviews with pharmacists, physicians and nurse practitioners from the 23 teams that integrated a new pharmacist role. Four researchers from varied backgrounds used thematic analysis of the interview transcripts to determine key themes. The research team met on multiple occasions to agree on the key themes and received written feedback from an external auditor and two of the original interviewees. KEY FINDINGS: Seven key themes emerged describing the barriers and facilitators that the teams experienced during the pharmacist integration: (1) relationships, trust and respect; (2) pharmacist role definition; (3) orientation and support; (4) pharmacist personality and professional experience; (5) pharmacist presence and visibility; (6) resources and funding; and (7) value of the pharmacist role. Teams from urban and rural communities experienced some of these challenges in unique ways. CONCLUSIONS: Primary care teams that integrated a pharmacist experienced several common barriers and facilitators. The negative impact of these barriers can be mitigated with effective planning and support that is individualized for the type of community where the team is located.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".