Global Status of Pharmacist-Administered Vaccinations
Bibliographic record
Abstract
This study investigates the role of pharmacist-administered vaccinations in member countries of the International Pharmaceutical Federation (FIP) with the objective of providing insights for developing a pharmacist vaccination model in South Korea. It explores the expanding responsibilities of pharmacists in vaccination practices across nine countries, including South Africa, Kenya, Ireland, the United Kingdom, Portugal, Canada, the United States, Australia, and New Zealand, with a focus on variations in training, certification, and regulatory frameworks. In these countries, pharmacists are authorized to prescribe vaccines and are required to complete certifications in CPR and first aid, along with acquiring the necessary skills to manage emergency situations such as anaphylaxis. Regular renewal of these certifications is typically mandated, underscoring the importance of keeping up-to-date with the latest vaccination knowledge. The study reveals that pharmacists in all nine countries are permitted to administer vaccines for influenza, COVID-19, and pneumococcal disease, while vaccines such as hepatitis B, HPV, and Tdap are authorized in a subset of these nations. Additionally, countries like Kenya and New Zealand provide pharmacists with broader authority to administer a wider array of vaccines. The research underscores the critical role of pharmacists in enhancing vaccine access, particularly in regions with limited healthcare infrastructure, and highlights the need for standardized policies and updated training programs to strengthen their role in global vaccination efforts, thereby improving public health outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".