A scoping review of evidence of community pharmacist independent prescribing for common clinical conditions: beyond protocol prescribing
Bibliographic record
Abstract
OBJECTIVES: Pharmacist independent prescribers (PIPs) enhance patient care but there is variability in their integration in community pharmacy (CP). The objective was to collate and characterize literature on the integration of 'standard of care' model PIPs in CP for acute common clinical conditions (CCCs). METHODS: This review followed the Arksey and O'Malley framework. Eligibility criteria, search databases, and terms were defined. Information sources were searched from January 2006 to October 2023. Following screening, full text review, and data extraction a narrative synthesis approach was used to address the review objectives. All steps were independently checked by two of the review team. Barriers and facilitators for integration used the Consolidated Framework for Implementation Research as a theoretical lens. KEY FINDINGS: Ten papers remained for full text review from 1075 records. Most studies were from Canada and focused on pharmacist views, evaluation of safety, effectiveness, and patient satisfaction. A range of CCCs were included with a focus on antimicrobial prescribing. A wide range of barriers and facilitators influencing implementation were identified including; 'regulatory constraints' and 'fiscal challenges' at a macro socio-organizational level and several challenges within organizations; lack of clarity on the pharmacists' scopes of practice and linked consumer confusion, staffing levels, and workload with specific mention of paperwork and access to patient records. CONCLUSIONS: Most evidence for CCC management by PIPs relates to antimicrobials, originates in Canada and identifies multiple challenges. Given this there is a need to consider this topic further to identify ways to address the challenges and facilitate integration of PIPs in CP.
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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 | 0.060 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".