Virtual care in community pharmacy services: a scoping review
Bibliographic record
Abstract
BACKGROUND: The integration of virtual care has been essential for maintaining continuity of patient care during and after the COVID-19 pandemic. Community pharmacists were among the healthcare professionals who used virtual care to provide remote pharmacy services. However, the use of virtual care in community pharmacy has not been comprehensively reviewed. OBJECTIVE: To provide an overview of the types of virtual care platforms used in community pharmacy, their purposes, barriers, and facilitators, and to identify strategies for optimizing virtual care in this setting. METHODS: The scoping review followed Joanna Briggs Institute (JBI) methodology for scoping reviews. A comprehensive search strategy was employed to identify studies across MEDLINE, Embase, CINAHL, Scopus, and grey literature sources. Two levels of screening were performed, and data were extracted using a pre-specified form. The data were analyzed through qualitative content analysis, and the PRISMA-ScR was used to report the results. RESULTS: A total of 3580 citations were assessed and 32 studies were included in the review. The qualitative data were summarized into five categories: (1) virtual care tools and technologies used in community pharmacy, (2) purpose of virtual care, (3) barriers to using virtual care, (4) facilitators of virtual care adoption, and (5) strategies to optimize virtual care use. CONCLUSION: Virtual care in community pharmacy showed promise in enhancing pharmacy services. Despite operational barriers and inequities in access, facilitators such as adequate training and technological advancements supported its implementation. Future efforts should address these challenges and refine virtual care for broader and equitable integration.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 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".