Assessment of perspectives, knowledge and attitude about antibiotic use and resistance in Sudanese population
Bibliographic record
Abstract
INTRODUCTION: The spread of multidrug-resistant pathogens is a major global health concern. A survey was conducted to evaluate the knowledge and attitudes towards antimicrobial use and resistance in Sudan. METHODOLOGY: A cross-sectional survey with a 39-item questionnaire was distributed via social media platforms to Sudanese residents in Khartoum state. Responses were collected anonymously from April to October 2022 and subjected to statistical analysis to assess associations between variables. RESULTS: A total of 1,037 participants agreed to participate, with a 94.3% response rate. Two-thirds of participants reported using oral antibiotics in the past 12 months. Only a quarter obtained antibiotics with a prescription. Less than half (45.3%) of the participants underwent diagnostic tests before using antibiotics, and 30.2% adjusted or discontinued the antibiotic dosage. Forty-two percent correctly identified that antibiotics are ineffective against viral infections, but confusion regarding their use persisted. The mean knowledge score was 3.3 ± 1.7, indicating average knowledge levels. Significant variations in knowledge and attitudes were observed based on age, gender, marital status, and education. The mean score of the participants' attitude was 25.5 ± 3.97. Female, younger, and single participants exhibited more positive attitudes towards antibiotics use and resistance. CONCLUSIONS: The participants exhibited average knowledge levels and mixed attitudes towards antibiotic use and resistance. Misconceptions and inadequate indications for antibiotic use were identified. Gender, age, marital status, and education influenced participants` knowledge and attitudes. These findings can inform strategies to promote appropriate practices and combat the spread of antibiotic resistance across health and non-health sectors.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".