P14 Knowledge, attitude and practices on antibiotic usage and resistance among people attending primary healthcare in Rwanda
Bibliographic record
Abstract
Abstract Background Antimicrobial resistance (AMR) poses a global threat to public health, with Sub-Sahara African countries facing a substantial burden. Our study aimed to assess knowledge, attitude and awareness of antibiotic usage and resistance among people attending primary healthcare facilities in Rwanda. Methods The study was descriptive cross-sectional and proportionate stratified sampling was used to recruit 246 individuals who attended health centres in Kigali during October 2023. The study assessed the level of knowledge on antibiotic usage, knowledge on antimicrobial resistance, attitudes toward antibiotic accessibility and practices regarding the patient-prescriber relationship and infection prevention. The levels were calculated as proportions of correct answers and were grouped as poor (below 40%), moderate (from 40 and less than 70%) and high, good or positive (70% and higher). χ2 test was used find significance of associations between levels and age, gender and education. Results Most participants (87.40%) correctly identified Amoxicillin as an antibiotic while 57.72% wrongly identified Paracetamol as an antibiotic. Among participants, 3.2% had high knowledge on antibiotic usage, 20.7% had high knowledge on antimicrobial resistance, 32.9% had a positive attitude toward antibiotic accessibility and 39.4% had good practices regarding the patient-prescriber relationship and infection control. Male participants had significantly higher levels of positive attitude (P=0.003) and knowledge on antimicrobial resistance (P=0.047). Individuals with university education had significantly higher levels of positive attitude (P=0.007). Conclusions Limited levels of knowledge, attitude and practices on antibiotic usage and resistance were found, with women having lower levels in multiple aspects. Strategies to promote rational use of antibiotics ought to address social inequities.
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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 | 0.000 | 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) | 0.003 | 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".