Application of Dengue Hemorrhagic Fever Information System (SI-DBD) for Recording and Reporting of DHF Suspects at Kota Public Health Centers in Bantaeng Regency
Bibliographic record
Abstract
Background: Dengue fever is the most common viral infection transmitted by Aedes mosquitoes. This disease puts more than 3.9 billion people from 129 countries at risk of contracting dengue fever and causes 40,000 deaths each year. This study aims to analyze the effectiveness of SI-DBD applications for finding, recording, and reporting suspected cases of dengue.
 Methods: This type of research is a quasi-experiment with The Nonrandomized Control Group Pretest Posttest Design, namely there were two treatment groups (SI-DBD application users) at RT 02 and (a control group) at RT. 01, with a sample of 112 households (1:1 ratio). Data was collected through interviews and reports of suspected dengue fever.
 Results: There was an increase in reporting of suspected dengue after using the Application of the Dengue Hemorrhagic Fever Information System (SI-DBD) (233.33%). Statistical test results in the intervention group's simplicity, acceptability, data quality, and timeliness had p < 0.000, meaning that there were significant differences in all variables studied in the reporting system using the SI-DBD application. In the control group, statistical tests showed that the acceptability variable had a p < 0.0001, meaning that there were significant differences in the acceptability variable in the use of the manual system before and during the study while the variables were for simplicity, data quality, and timeliness had a p > 0.1797, 0.0833, 0.5567 means that there is no significant difference in these variables in the manual reporting system.
 Conclusion: SI-DBD application is effective for recording and reporting suspected dengue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.025 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".