‘The disease isn't listening to the drug’: The socio-cultural context of antibiotic use for viral respiratory infections in rural Uganda
Bibliographic record
Abstract
To identify factors precipitating antibiotic misuse and discuss how to promote safe antibiotics use and curb antibiotic resistance. Antibiotic misuse is a significant problem globally, leading to increased antibiotic resistance. Many socio-cultural factors facilitate antibiotic misuse: patient and provider beliefs about antibiotics, inadequate regulation, poor health literacy, inadequate healthcare provider training, and sub-optimal diagnostic capability. This study investigates the influence of such factors on antibiotic use and community health in rural Uganda. Attention was paid to patient-provider dynamics, providers' concerns, and the role of drug shops in the communities and how these situations exacerbate antibiotic misuse. Using a grounded ethnographic approach, interviews, focus groups, and observations were conducted over six weeks. Five salient themes emerged from data analysis. Based on the study results and a review of past literature on antibiotic resistance, there is need for improved health literacy and education, continued focus on efficiency and affordability in healthcare, and recognition of the role of stewardship and government in providing better healthcare. The problem of antibiotic misuse is multifactorial. Proposed solutions must target multiple contributing factors and must ultimately modify the culture and beliefs surrounding antibiotic use and encourage proper use. Such a multi-pronged approach would be most effective and would decrease rates of antibiotic resistance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".