Family physicians’ perceptions of pharmacists prescribing in Alberta
Bibliographic record
Abstract
Canadian pharmacists now have prescribing authority and little is documented about the physicians’ perception, experience and relational dynamics evolving around the pharmacists’ prescribing practice. The objective of this study was to explore Albertan family physicians’ perceptions and experiences of pharmacists’ prescribing practice. We used purposeful and maximum variation sampling method and semi-structured face to face or telephone interviews to collect data. From October 2014 to February 2016, we interviewed 12 family physicians in Alberta, having experience with pharmacist prescribing. Interviews were audio recorded and transcribed verbatim for analysis using an interpretive description method, guided by “Relational Coordination” theory. NVivo software was used to manage the data. Three key beliefs (i.e., renewal versus initiate new prescription, community versus team pharmacists, and “I am responsible”) about pharmacist prescribing were identified. Trust and communication were prominent themes which shaped participants‘ collaboration with pharmacist prescribers. Participants were classified as either “collaborative” or “consultative”. Participants had greater collaboration with the team pharmacist prescribers compared to community pharmacists due to a higher level of trust and ease of communication. Renewal prescribing by any pharmacist was well accepted but participants showed hesitancy in accepting pharmacist-initiated prescriptions. Our findings provide insight into interprofessional collaboration and communication between physician and pharmacist prescribers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".