Pharmacists as independent prescribers in community pharmacy: A scoping review
Bibliographic record
Abstract
BACKGROUND: There has been a growing interest in granting prescribing rights to pharmacists as a strategy to improve healthcare access. Researchers continue to explore the impact and implementation of pharmacist prescribing. Given the recent international changes in this field, an overview of current territories allowing pharmacist independent prescribing would provide a comprehensive understanding for researchers and policymakers. AIM: This scoping review aims to summarize the countries and specific jurisdictions where pharmacists can prescribe independently in community pharmacy, and map the conditions they can prescribe for, required training, and reimbursement policies. METHOD: This scoping review was conducted in October 2024 and has been reported following the PRISMA-ScR guidelines. Searches were performed in Scopus, Web of Science, CINAHL, PubMed, and Cochrane databases, along with grey literature searches using Google. RESULTS: A total of 88 studies and reports were identified. The countries where pharmacist can prescribe independently include the United Kingdom, the United States, Canada, Australia, Poland, Switzerland, and Denmark. Pharmacists authorized as independent prescribers generally require post-registration training and are authorized to initiate, adapt, renew, or substitute prescriptions. For the payment and reimbursement, this service is publicly funded only in Canada, Denmark, France, and the United Kingdom. CONCLUSION: Pharmacist prescribing practices vary significantly worldwide, with differences in terminology, legislation, and training requirements. This scoping review provides the necessary information to visualize and conceptualize the current scope of pharmacist independent prescribers, offering a foundation for advancing this practice in new jurisdictions. Further research should address current models in under-studied regions, explore the scope for pharmacists to prescribe for undiagnosed conditions, and analyze payment structures in non-funded jurisdictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".