DETERMINAN PERILAKU TERHADAP PENGGUNAAN ANTIBIOTIK PADA BALITA DI WILAYAH KERJA PUSKESMAS KUBU II KARANGASEM
Bibliographic record
Abstract
Introduction: Antibiotics are recognized as crucial therapeutic compounds in the prevention and management of infections, especially in the pediatric population. In Indonesia, infectious diseases persist as the leading contributors to both morbidity and mortality in children under five, triggering an urgent need for quick and efficient medical interventions. In clinical practice, antibiotics are often chosen as the primary response by parents and medical personnel due to their perceived instant effectiveness. However, the use of antibiotics without proper scientific and policy considerations have the capacity to induce enduring negative consequences, affecting not only individuals but also reverberating across global scale. Objective: This research’s objective is to elaborate the influence of occupation, knowledge, education level, gender, age, and perception on the behavior of Antibiotic use behavior in toddlers at Public Health Center whose working areas are in Kubu II. Method: This study used a cross-sectional quantitative study method. This study has a population of parents of toddlers aged 0 to 59 months in Tianyar Tengah Village whose working area is in Kubu II, totaling 186 participants. Results: This research reveals that some predictors such as occupation, educational attainment, knowledge, and perception play a crucial role in shaping antibiotic usage patterns among children under five. While gender and age did unsignificantly affected. The educational level emerges as the most significant predictor influencing antibiotic usage patterns in children under five. Conclusion: Education level was the most influential predictor of antibiotic use in under-fives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".