Knowledge, Attitude, and Practices of Antibiotic Usage and Resistance among People Attending Primary Healthcare in Rwanda
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) poses a global threat to public health with sub-Saharan Africa facing a substantial burden. Our study assessed the knowledge, attitude, and practices of antibiotic usage and resistance among people attending primary healthcare facilities in Rwanda. Methods: The cross-sectional study was conducted at three health centres in Kigali, and it involved 246 individuals. We used a close-ended questionnaire for data collection. The levels of knowledge, attitudes and practices were calculated as proportions of correct answers, with high, good, or positive being greater or equal to 70%. The chi-square test was used to find the association between demographic characteristics and knowledge, attitudes and practices. Results: Among 246 participants, 8 (3.2%) and 51 (20.7%) had high knowledge of antibiotic usage and antimicrobial resistance respectively. In addition, 81 (32.9%) had a positive attitude and 97 (39.4%) had good practices. Attitudes were significantly positive in males (p = 0.003) and among individuals with a university education (p = 0.007). Knowledge of antimicrobial resistance was significantly high in males (p-value = 0.047). Conclusion: 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 | 0.000 |
| 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) | 0.002 | 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".