COVID-19: How did community pharmacies get through the first wave?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic of early 2020 was one of the most impactful events in living memory. As an essential service, community pharmacies remained open to provide care and service. The unprecedented nature and scale of the pandemic triggered considerable change in daily practice. In anticipation of future pandemic waves and similar mass-scale civil disruptions, it is important to understand how community pharmacies adapted and responded in the early weeks of COVID-19. Methods: A combination of convenience, snowball and purposive sampling methods was used to recruit staff from community pharmacies across Ontario, from a variety of different practice locations and types. A semistructured focus group interview protocol was used to elicit experiences. Data gathering was undertaken until the point of saturation. Thematic analysis was used to surface common experiences and to describe how community pharmacies adapted and responded. Results: A total of 39 participants (pharmacists, registered technicians and assistants) from 11 different pharmacies participated in this study. Data were coded based on 1) what happened, 2) how community pharmacies responded, and 3) what worked and did not work to support pharmacy staff in continued provision of service and care. Key findings included the collapse of provision of nondispensing remunerated services, the central role of managerial decisions in supporting resilience (e.g., change to 8-hour shifts from 12-hour shifts) and the central role of technology in supporting continuity of quality pharmacy services. Discussion: With anticipated future pandemic waves, preparedness of community pharmacy will be essential. This study provides important insights based on participants’ own experiences regarding ways employers can better support staff in provision of care and service to patients during times of mass-scale civil disruption. Can Pharm J (Ott) 2020;153:xx-xx.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it