Independent Pharmacist Prescribing in Canada
Bibliographic record
Abstract
BACKGROUND: While pharmacists are trained in the selection and management of prescription medicines, traditionally their role in prescribing has been limited. In the past 5 years, many provinces have expanded the pharmacy scope of practice. However, there has been no previous systematic investigation and comparison of these policies. METHODS: We performed a comprehensive policy review and comparison of pharmacist prescribing policies in Canadian provinces in August 2010. Our review focused on documents, regulations and interviews with officials from the relevant government and professional bodies. We focused on policies that allowed community pharmacists to independently continue, adapt (modify) and initiate prescriptions. RESULTS: Pharmacists could independently prescribe in 7 of 10 provinces, including continuing existing prescriptions (7 provinces), adapting existing prescriptions (4 provinces) and initiating new prescriptions (3 provinces). However, there was significant heterogeneity between provinces in the rules governing each function. CONCLUSIONS: The legislated ability of pharmacists to independently prescribe in a community setting has substantially increased in Canada over the past 5 years and looks poised to expand further in the near future. Moving forward, these programs must be evaluated and compared on issues such as patient outcomes and safety, professional development, human resources and reimbursement.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".