Comprehensive Analysis of Knowledge, Perception, and Preparedness of Ghanaian Pharmacists Towards a Pandemic or Another Wave of COVID-19
Bibliographic record
Abstract
Despite the decline in infection and death rates, COVID-19 remains a significant global health concern. This study delves into Ghanaian pharmacists' knowledge, perception, and preparedness towards a pandemic or another wave of COVID-19. A cross-sectional survey was conducted among pharmacists across all 16 regions of Ghana between May and July of 2023, with a total of 1199 responses recorded. The data was analyzed using IBM Statistical Product and Service Solution (SPSS). Of the respondents, 629 (52.5%) were males, while 570 (47.5%) were females. Our study reveals that 98% of the participants provided positive feedback about knowledge-related questions. The study also found an adequate understanding of pharmacists' attitudes toward coronavirus symptoms, transmission, disease severity, and preventive measures. Ghanaian pharmacists' responses toward the perceived susceptibility to COVID-19 were analyzed using questions related to disease contamination, contracting, and fear level due to the disease. The optimistic behaviour and perception of Ghanaian pharmacists were commendable. However, only 45% of the pharmacists were confident about their level of preparedness, underlining the urgent need for updated information and infection control policies. Infection control policies with updated information should be available for all healthcare professionals. Moreover, Ghana needs a blueprint for pandemic management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".