Assessment of inappropriate use of antibiotics and contributing factors in Awi Administrative Zone, Northwestern Amhara regional State, Ethiopia
Bibliographic record
Abstract
Objectives: Antibiotic misuse is regarded as the single most significant factor contributing to resistance. Thus, this study aimed to evaluate the prevalence and risk variables linked to the inappropriate use of antibiotics in urban and rural districts of the Awi administrative zone community. Methods: A total of 1194 rural and urban families, including individuals of various ages and genders from the study area were selected by a multistage stratified random sampling method for a comparative cross-sectional study conducted between December 2022 and June 2023. SPSS version 26 was used to analyze the gathered data. Descriptive statistics and logistic regression analysis methods were used to identify the variables linked to the incorrect use of antibiotics. The adjusted odds ratio was used to calculate the statistical significance of the correlation at a significance level of 5 %. Results: The findings revealed that, in urban and rural regions, 57.5 % and 69.5 % of the households used unsafe antibiotic practices. The logistic regression analysis showed a significant relationship between the inappropriate use of antibiotics and the household head's age, marital status, family size, monthly income, occupation, educational attainment, place of residence, knowledge of antibiotics, and practice of using antibiotics. Conclusion: The study area has inappropriate antibiotic use, with statistically significant differences between urban and rural communities. Extensive educational (knowledge and practice) interventions are required to enhance the appropriate use of antibiotics. To guarantee that antibiotics are dispensed correctly and that the right information is provided regarding how the antibiotic functions and should be used, authorized entities should strengthen their regulatory enforcement at pharmacies.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".