Healthcare seeking behavior and antibiotic use for diarrhea among children in rural Bangladesh before seeking care at a healthcare facility
Bibliographic record
Abstract
Appropriate healthcare utilization and compliance with the WHO treatment guidelines can significantly reduce diarrhea-related childhood mortality and morbidity, while overuse of antibiotics notably increases antibiotic resistance. We studied care-seeking behavior and antibiotic use for childhood diarrhea by analyzing data from 8294 diarrheal episodes of 1-59-month-old children visiting a tertiary-care hospital in rural Bangladesh. Overall, 55% of the study children received antibiotics, while only 6% had dysentery. Notably, 77% of the antibiotics were obtained from a local pharmacy without a prescription. Antibiotics alone, without zinc or ORS, were used by more children with dysentery than watery diarrhea (15% vs. 9%; p < 0.001). While 85% of the children received ORS, only 7% received zinc and ORS without antibiotics. Children who received antibiotics before seeking care at the hospital had a significantly higher rate of hospitalization than those who did not have antibiotics (20% vs. 13%; p < 0.001). The factors that influenced the caregivers' decision to seek care from the pharmacy were the desire for early recovery, traditional practices, faith in seeking care at pharmacies, and distance to a healthcare facility. Our findings warrant that reducing unnecessary antibiotic consumption requires increasing public awareness and strengthening laws on the sale of over-the-counter antibiotics.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".