Flu Vaccinations in Pharmacies—A Review of Pharmacists Fighting Pandemics and Infectious Diseases
Bibliographic record
Abstract
The phenomenon of population ageing observed over recent years involves growing healthcare needs and the limited staffing and financing of healthcare systems, and as such demands some functional changes in the healthcare model in many countries. This situation is particularly significant in the face of a pandemic, e.g., flu, and currently COVID-19.As well as social education, preventive vaccinations are the most effective method of fighting the infectious diseases posing a special threat to seniors. Despite this, the vaccination coverage level in most European countries is relatively low. This is largely due to patients having limited access to vaccinations. In some countries, implementing vaccinations in pharmacies and by authorized pharmacists has significantly improved vaccination coverage rates and herd immunity, while lowering the cost of treating infections and the resulting complications, as well as minimizing the phenomenon of inappropriate antibiotic therapies. This article presents the role of pharmacists in the prevention of infectious diseases, pointing out the measurable effects of engaging pharmacists in conducting preventive vaccinations, as well as analyzing the models of implementing and conducting vaccinations in pharmacies in selected countries, and depicting recommendations regarding vaccinations developed by international organizations. The presented data is used to suggest requirements for the implementation of preventive vaccinations in community pharmacies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".