Antibiotic misuse: a public health care risk in the community
Bibliographic record
Abstract
Antibiotic abuse leads to the development of antibiotic resistance mechanisms. The misuse of antibiotics has been identified by the World Health Organization and the European Union as one of the most serious threats to public health due to its association with increased disease duration, mortality rate and health care costs. Thus, the over-registration and overuse of antibiotics is considered a matter of utmost importance in the global community. The factors found to affect the correct antibiotic usage were age, gender and education level. People older than 60 years of age were found more likely to misuse antibiotics compared to younger people. Most antibiotics were found to be used by women and people with a lower level of education. According to a European study, the average antibiotic consumption was 21.8 doses / day per 1000 inhabitants in the community, while in Greece it was found to be 36.3 doses / day. 26.7% of Greeks are supplied with non-prescription antibiotics, while 56% of those are supplied by pharmacies and 30% selfmedicate. Distribution of antibiotics occurs mainly through medical prescription, however 69% of doctors questioned declared to being pressured to prescribe antibiotics without need. At the same time, 65% of pharmacists admitted to giving antibiotics without a prescription, while 46% of those state that they received intense pressure by patients. Community Nurses provide a reliable source of information on this topic, while also excel at informing and educating the masses on the proper use of antibiotics. Intervention using awareness campaigns and introductory programs for the adoption of proper practices regarding antibiotics, organized by Primary Health Care Nurses, should be aimed at health professionals and the general population, thus minimizing the current situation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".