Is the level of knowledge a predictor of rational antibiotic use in Serbia?
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to investigate the knowledge, attitudes and behavior regarding antibiotics of the general population. METHODS: The study sample consisted of adult subjects who consulted general practitioners at health centers in Serbia and agreed to complete the questionnaire. A total of 668 questionnaires were distributed; 500 respondents completed the entire questionnaire (response rate 74.85%). RESULTS: The average age was 51.65 ± 16.56 years, 60.80% of the respondents were women. The median antibiotic knowledge score was 9. Predictors of adequate antibiotic knowledge were higher education level and a family member whose ocuppation was related to health-care. Overall, 58.4% of respondents believed that antibiotics could be used to treat common cold. Around a half of the respondents (47.2%) self-medicated with antibiotics at least once during their life-time, and around a quarter (24.2%) during the last treatment of infection. Patients with inadequate knowledge had 3 times higher chances of self-medicating with antibiotics compared to those with adequate knowledge. Although 98.20% of respondents claimed that antibiotic treatment should be started after a visit to a doctor and receiving a prescription, only 65.8% obtained antibiotics with prescription from a doctor during the last infection. CONCLUSIONS: Although these results demonstrate that respondents had a relatively adequate level of knowledge regarding antibiotics use, some areas of misconceptions and improper behavior were identified. Therefore, further rationalization should be focused on educational campaigns targeting the behavior of patients with regard to antibiotic 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".