Public knowledge, attitudes and practices regarding antibiotic use in Kosovo
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance is becoming a major public health challenge worldwide, caused primarily by the misuse of antibiotics. Antibiotic use is closely related to the knowledge, attitudes and behaviour of a population. OBJECTIVE: The objective of this study was to assess the level of knowledge, attitudes and practices about antibiotic use among the general public in Kosovo. METHODS: A cross-sectional face-to-face survey was carried out with a sample of 811 randomly selected Kosovo residents. The methodology used for this survey was based on the European Commission Eurobarometer survey on antimicrobial resistance. RESULTS: More than half of respondents (58.7%) have used antibiotics during the past year. A quarter of respondents consumed antibiotics without a medical prescription. The most common reasons for usage were flu (23.8%), followed by sore throat (20.2%), cold (13%) and common cold (7.6%). 42.5% of respondents think that antibiotics are effective against viral infections. Almost half of respondents (46.7%) received information about the unnecessary use of antibiotics and 32.5% of them report having changed their views and behaviours after receiving this information. Health care workers were identified as the most trustworthy source of information on antibiotic use (67.2%). CONCLUSION: These results provide quantitative baseline data on Kosovar knowledge, attitudes and practice regarding the use of antibiotic. These findings have potential to empower educational campaigns to promote the prudent use of antibiotics in both community and health care settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".