Global Strategies to Strengthen Community Pharmacists in Providing Primary Healthcare Services: Scoping Review
Bibliographic record
Abstract
Background: Community pharmacists (CPs) are pivotal in healthcare delivery; they are the first point of contact for healthcare services and are trusted by patients. Their role goes beyond the dispensing of medications to encompass emergency care and management, health promotion, and management of chronic diseases. The review aimed to explore strategies to strengthen the role of CPs in global primary healthcare service provision. Methods: The search followed a framework described by Arksey and O'Malley, subsequently expanded by Levac et al.. An extensive search of relevant academic databases yielded 4530 articles for review. Three databases (PubMed, Embase, Google Scholar) were searched using a combination of keywords and controlled vocabulary terms related to “primary healthcare services”, “communitypharmacist”, and “strengthening strategies.” They were combined using Boolean Operators ('AND', 'OR', 'NOT'). Selection and elimination based on the eligibility criteria identified 18 relevant articles. Data analysis entailed a systematic approach, including the categorisation of strategies and the identification of common themes across the regions. Results: The review demonstrated various strategies in different parts of the world, particularly policy support, professional development, collaboration, financial incentives, and public health initiatives. For instance, Portugal, the United States, and Canada demonstrated examples of policy and legislative support that allow pharmacists to expand their roles. In addition, financial conditions, seen in Indonesia, Canada, and the Netherlands, play a crucial role in the sustainability of pharmacist-led services. Britain, Europe, Portugal, Sudan, and Nigeria revealed the potential of pharmacists in public health, including health promotion and disease prevention. Conclusion: The review highlighted the significance of integrating CPs into primary healthcare roles that could potentially improve access, quality, and equity in the healthcare system. However, the issue of regulatory barriers, the necessity of capacity building, and financial reimbursement have to be recognised as current challenges requiring much effort and collaboration.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".