Knowledge, Attitude, and Practice Towards COVID-19 Among Pharmacists: A Cross-Sectional Study
Bibliographic record
Abstract
PURPOSE: The COVID-19 outbreak has caused governments to put pandemic-related guidelines requiring compliance and understanding by healthcare professionals to mitigate its spread uncontrollably. We studied pharmacists' knowledge, attitude, and practice towards the COVD-19 outbreak compared with other healthcare workers during the pandemic in Saudi Arabia. METHODS: We surveyed pharmacists' socio-demographics (n=50) compared with other healthcare professionals (n=378) during lockdown starting in June 2020. We measured respondents' level of knowledge (n=10 questions, maximum score of 10), attitude (n=17 questions, maximum score of 80), and their practices (n=16 questions, maximum score of 80) towards COVID-19 infection. RESULTS: Median knowledge score was 8 (25th-75th percentiles: 7-9), attitude score 76 (70-80) and practice score 74 (68-78). Good knowledge predictors were >20 years working experience [OR: 2.05 (95% CI: 1.03-4.06); P=0.04] and >50% working in clinical practice [OR: 1.72 (95% CI: 1.12-2.66); P=0.01], in inverse relationship with paramedical professions [OR: 0.45 (95% CI: 0.45 (0.28-0.72)); P=0.001] and working in a university hospital [OR: 0.51 (95% CI: 0.33. 0.81); P=0.004]. Availability of pharmaceutical information and treatment options was associated with good attitude [OR: 2.19 (95% CI: 1.04-4.59); P=0.039] and acquaintance as primary information sources negatively associated with good attitude [OR: 0.34 (95% CI: 0.15-0.8); P=0.013]. Good practice predictors were female gender [OR: 3.84 (95% CI: 2.37-6.24); P<0.001], military hospital employment [OR: 2.32 (95% CI: 1.25-4.31); P=0.008], USA [OR: 3.41 (95% CI: 1.03-11.22); P=0.044] or UK [OR: 8.86 (95% CI: 1.91-41.07); P=0.005] qualifications, and information on supportive measures [OR: 2.2 (95% CI: 1.36-3.56); P=0.001]. CONCLUSION: Health workers displayed good knowledge about COVID-19, while profession and working experience predicted adequate knowledge, positive attitude, or practice towards disease management.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".