Community Pharmacist Prescribing: Roles and Competencies—A Systematic Review and Implications
Bibliographic record
Abstract
Increasing healthcare demands and physician shortages have prompted many countries to expand clinical responsibilities of pharmacists. Although Canada, the UK, and the US have implemented pharmacist prescribing, other nations lag behind. This review compares international roles, identifies inferred competencies, and explores implications for role expansion. A systematic search of MEDLINE, CINAHL, and the Cochrane Library was conducted using the PICO framework; studies were appraised with Critical Appraisal Skills Programme (CASP) checklists, and interrater reliability assessed via Cohen's Kappa. Data from 23 studies were thematically synthesized following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Four themes emerged: (1) expanding clinical and public health roles and pharmacists' self-perceived readiness; (2) regulatory frameworks defining legal authority, qualifications, and temporary pandemic exemptions; (3) inferred competencies, including micro-skills (patient assessment, guideline application) and macro-capabilities (clinical judgment, accountability, reflective practice); and (4) contextual barriers such as training gaps, limited funding, unclear legal provisions, and workflow challenges. Implementation implications were synthesized and included training, funding, acceptance, and integration. Evidence indicates pharmacist prescribing is safe and patient-centered when supported by regulation, structured training, and systemic integration. Insights from established models can guide incremental implementation, optimizing medication management, enhancing healthcare access, and promoting equitable care.
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 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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".