Raising public awareness of the pharmacists’ involvement in the fight against the COVID-19 pandemic – the effects of Pharmacy Heroes campaign
Bibliographic record
Abstract
Background: The COVID-19 pandemic has significantly impacted health systems around the world. The healthcare burden was visible in all countries struggling with the pandemic. Pharmacists, who are at the frontline beating the COVID-19 pandemic, played a significant role in relieving the burden on healthcare systems. However, the role of pharmacists in the fight against the COVID-19 pandemic was not appreciated. Therefore, our goal was to create the Pharmacy Heroes initiative, which brings together pharmacists from around the world during the COVID-19 pandemic and promotes the role of pharmacists in the health care system. Aim: The article aims to present the results of the Pharmacy Heroes campaign, mainly in terms of dissemination. Results: 85 countries from six continents were involved in the Pharmacy Heroes campaign. Pharmacists who joined the campaign worked in both community and hospital pharmacies. Conclusion: The Pharmacy Heroes campaign showed the significant commitment of pharmacists around the world. Being the third-largest group of medical professionals, pharmacists played a crucial role in fighting the COVID-19 pandemic.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".