Pharmacists’ Perception of their Roles and Involvement in Coronavirus Disease 2019 (COVID-19)
Bibliographic record
Abstract
Background: Coronavirus disease 2019 was declared a “public health emergency of international concern” in January 2020 and a pandemic in March 2020 by WHO. With lockdown observed globally, there is greater dependence on pharmacists as the first point of contact to meet the public’s healthcare needs. However, the roles of pharmacists have not been clearly defined. Objectives: To document pharmacists’ perceptions of their roles in the COVID 19 outbreak and adequacy of training for emergency/pandemic situations. Methods: An online survey using pharmacists WhatsApp groups was carried out. Sample size was calculated as 384. A mobile App, FormsApp®, was used to create and disseminate the survey among pharmacists’ WhatsApp groups. Collected data was exported to Microsoft Excel and descriptive and thematic analysis with coding carried out. Ethical approval was obtained from the Lagos University Teaching Hospital (LUTH), Idiaraba, Lagos. Results: A total of 716 respondents participated in the study. The result shows 56% female participation, and respondents’ mean age as 39.04 ±10.46 years. Most common roles by respondents are counselling and advice (95%), information dissemination (91%) and sales of protective gear (60%). About 47% of the respondents believe pharmacists are adequately trained for emergencies while less than a quarter (24.3%) rated pharmacists’ involvement in COVID-19 pandemic as fully involved. Conclusion: From the study, pharmacists identified health education and counselling; production of sanitizers/PPE and drug therapy management as key roles for pharmacists in the pandemic while to improve involvement, training of pharmacists, provision of PPE and collaboration with emergency teams were identified.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".