Evaluation of the general public's knowledge, views and practices relating to appropriate antibiotic use in Qatar†
Bibliographic record
Abstract
OBJECTIVE: Studies completed internationally have demonstrated an alarming number of patients believed antibiotics are indicated in the treatment of viral infections and other self-limited illnesses. Evaluation of patient practices relating to antibiotics have also demonstrated inappropriate use. Antibiotic misuse by patients and practitioners has been identified as a factor in the development of resistance. Current knowledge, views and practices relating to antibiotic use in Qatar is unknown. The primary objective of this study was to evaluate the general population's current antimicrobial knowledge, views and practices in Qatar. METHODS: This study was designed as a self-administered cross-sectional survey. Eligible participants were residents of Qatar who were over the age of 18 and spoke English or Arabic. The questionnaire was developed based on previously published literature and objectives of this study. Data were collected at community pharmacies in Doha, Qatar. KEY FINDINGS: The majority of participants (95.8%) had taken antibiotics in the past. The median knowledge score of the study population was 4/8. Misconceptions relating to use of antibiotics for treatment of viral infections were common. Inappropriate use as evident by hoarding of antibiotics for future use and sharing antibiotics with family or friends was also identified in this study population. CONCLUSION: Community pharmacists in Qatar have an opportunity to improve knowledge of the general population regarding appropriate indications of antibiotics and risk of resistance with inappropriate use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".