Integrating into family practice: the experiences of pharmacists in Ontario, Canada
Bibliographic record
Abstract
Abstract Aims and objectives This research examines the experiences of pharmacists as they integrated and adapted to meet the drug-related needs of family practice settings. Setting This research took place in physician-led group family medicine practices in Ontario, Canada. Each practice was in the process of integrating an on-site pharmacist. Methods Qualitative design using monthly pharmacist narrative reports (over the first 4months of pharmacist integration) and N-VIVO qualitative analysis software. Four independent researchers with varied professional backgrounds used descriptive thematic editing analysis to determine process and content themes. The analysis team created a draft of themes and received written feedback from each pharmacist. Key findings Four key themes emerged describing how pharmacists experienced the first several months working in family practice: (1) feelings: emotional challenges and victories; (2) establishing and building relationships: positive and negative experiences with physicians and staff; (3) learning new skills to contribute effectively and efficiently to patient care; and (4) strategies for integration: including practical demonstration of potential value to physicians to facilitate integration process. In addition, they identified a number of supports and constraints for integration. Conclusion The pharmacists' narratives demonstrate the challenges and rewards of the integration process. Adaptability and practical demonstration of potential utilization and benefit were crucial in physician acceptance of the pharmacist program. This description of the pharmacists' journey will be helpful for pharmacists, managers, policy-makers, researchers and educators as more pharmacists enter this type of primary care practice.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".