The community pharmacist as an independent prescriber: a scoping review
Bibliographic record
Abstract
Abstract Background Due to the shortage of professionals, the ever-increasing needs and demands for appointments, consultation of physicians is getting harder, and delays are getting longer. To limit this issue, several countries have experimented different models of independent prescribing through pharmacies. Aim This study aimed to analyze the contribution of independent prescribing by community pharmacists, in primary care, using the micro, meso, macro level framework. Method Three different databases were used: Medline, Scopus and Embase. The search was executed on March 23rd, 2022, for each of them. Some relevant articles were added manually to the search. To be selected for the review, articles needed to be published after 2000, written in English or French, focus on independent prescribing by community pharmacists, and concern a minor condition. Original research was accepted. Articles were imported into the Covidence systematic review software to perform the review. Results The search resulted in 1768 articles and 846 after removing duplicates. After title and abstract screening, 792 articles were qualified as irrelevant, and 54 full text studies were reviewed for eligibility. Finally, eight studies were included in the scoping review. The various studies included are recent (from 2013 to 2021) and mostly of Canadian origin (6/8). For five of them, a quantitative study design is observed. As an independent prescriber, the community pharmacist can improve patient access to primary care, reducing consultation delays for minor conditions. In addition, patient safety is maintained, and the patient’s quality of life is increased. The pharmacist independent prescriber model seems to be cost-effective for society. Conclusion This review highlights the potential value and relevance of the pharmacist independent prescriber. The benefits to society are felt at all levels: micro, meso, and macro. Effectiveness and efficacy seem established, but further studies are needed in other fields, as efficiency.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".