KNOWLEDGE, ATTITUDE, AND PRACTICE (KAP) STUDY ON DENGUE FEVER IN ADULTS: AN ANALYSIS OF 200 PATIENTS
Bibliographic record
Abstract
Background: Dengue fever (DF), a viral illness transmitted by mosquitoes, poses a growing public health threat, particularly in tropical and subtropical regions. This study seeks to assess the KAP of 200 adult patients concerning dengue fever in a densely populated urban environment. Methods: A cross-sectional survey was conducted among 200 adult patients diagnosed with dengue and admitted to the medical units of Hayatabad Medical Complex between January 2023 and December 2023. The data were analyzed using descriptive statistics with SPSS version 26, revealing notable gaps between the patients' knowledge and their preventive behaviors. Results: Most participants (85%) were aware that dengue is transmitted by mosquitoes, and 78% knew that Aedes aegypti is the main vector. However, fewer recognized key transmission details. Preventive measures were less commonly adopted, with 70% using mosquito repellents or nets, but only 45% eliminating standing water. There was a notable gap between knowledge and practice. Discussion: The findings show a gap between what people know and what they actually do to prevent dengue. The study highlights the need for better public health efforts, focusing on practical solutions and creating supportive environments to encourage community-wide adoption of preventive measures. Conclusion: Despite adequate knowledge of dengue transmission and symptoms, preventive practices remain insufficient among adult patients. Public health strategies should focus on bridging this gap through community engagement and government-supported preventive measures.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".