Pharmacy response to COVID-19: lessons learnt from Canada
Bibliographic record
Abstract
When the first wave of COVID-19 hit in March 2020, health care professionals across Canada were challenged to quickly and efficiently adapt to change their work practices in these unprecedented times. Pharmacy professionals, being some of the very few front-line health care workers who remained accessible in person for patients, had to rapidly adopt critical changes in their pharmacies to respond in the best interest of their patients and their pharmacy staff. As challenging and demanding as such changes were, they provided pharmacists with invaluable lessons that would be imperative as the country enters a potentially more dangerous second wave. This article seeks to identify and summarize opportunities for improvement in pharmacy as learnt from the pandemic's first wave. Such areas include but are not limited to handling of drug shortage and addressing drug hoarding and stockpiling, providing physical and mental support for staff, timing of flu vaccine and COVID-19 screening/testing, collaboration between different health care sites as well as collaboration with patients and with other health care professionals, telemedicine and willingness to adopt innovative ideas, need for more staff training and more precise research to provide accurate information and finally the need for more organizational and workplace support. Learning from what went well and what did not work in the early stages of the pandemic is integral to ensure pharmacy professionals are better prepared to protect themselves and their patients amidst a second and possibly subsequent waves.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".