Pharmacists and COVID-19
Bibliographic record
Abstract
In the fight against COVID-19, frontline health workers have been vital to keeping the pandemic at bay, but recognition of individual professions' efforts have been inconsistent at all levels. Pharmacists around the world have continued to provide direct patient care and perform frontline duties for their communities during this pandemic, but are often relegated to the background and overlooked when frontline workers are heralded. Community pharmacists are the most accessible healthcare practitioners, which is further proven during the pandemic as they continued to provide direct patient care despite restrictions imposed by the government due to the pandemic. Due to the inaccessibility of other healthcare practitioners during this time, community pharmacists have reduced the burden on the healthcare system by diverting the influx of patients away from hospitals through triaging and screening patients. Community pharmacists have played various roles in supporting the healthcare system during COVID-19: delivering medications to patients, educating patients on telehealth services, assessing patients for renewal of chronic medications, performing consultations on minor ailments, clarifying misconceptions about COVID-19 treatments, and contributing to COVID-19 screening. Alongside ICU nurses, physicians, and respiratory therapists, hospital pharmacists have been part of the COVID-19 efforts and their roles include management of drug shortages, development of treatment protocols, participation of patient rounds, interpretation of lab results for COVID-19, participant recruitment for clinical trials, exploration of new drugs, medication management advice, and antimicrobial stewardship. Further support from pharmacists will be needed once a vaccine is launched in order to reach population-wide coverage. Amid COVID-19, pharmacists have not stopped working as frontline workers and they should be recognized as such.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".