How have pharmacists in different practice settings integrated prescribing privileges into practice in Alberta? A qualitative exploration
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: Since 2007, pharmacists in Alberta have had authority to adapt existing prescriptions and independently prescribe medications after a peer review process. This study aimed to explore and characterize how pharmacists incorporated prescribing into practice 3 years after this legislation was approved. METHODS: We invited pharmacists to participate in semi-structured telephone interviews to discuss their prescribing practices. Pharmacists working in community, primary care network, hospital or other settings were selected using a mix of purposive and random sampling. Two investigators independently analysed each transcript using an Interpretive Description approach and thematically categorized prescribing practices according to the level of adoption. RESULTS AND DISCUSSION: Thirty-eight pharmacists (n = 13 independent prescribers) participated. Eighteen (47%) had a primary practice site from community practice, eight (21%) primary care, five (13%) hospital practice and seven (18%) from other settings including specialty clinics and long-term care. Twenty-eight participants were categorized as adopters and ten as non-adopters in their primary practice setting. Prescribing practices adopted were characterized as product focused, disease focused or patient focused. Sixteen (42%) described product-focused prescribing where they continued an existing therapy or substituted medications based on formulary guidelines. Seven (18%) described disease-focused prescribing where current therapies were adapted or initiated based on a protocol in a specific therapeutic area. Five (13%) described patient-focused prescribing where they initiated therapy based on patient needs and values, their assessment of the patient and best evidence. Non-adopters were not prescribing, but many described provision of disease or patient-focused care where they influenced prescribing by interacting with other members of the healthcare team. Most commonly, community pharmacists participated in product-focused prescribing, whereas hospital and primary care pharmacists practised disease-focused prescribing. WHAT IS NEW AND CONCLUSION: Our data suggest that there have been context-related differences in uptake across practice settings. Despite this, pharmacists in all studied settings engaged in prescribing activities using three approaches and many pharmacists who were not directly prescribing medications reported having involvement in drug therapy decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".