Understanding the future of dengue in Malaysia: Assessing knowledge, attitude, and homeowner practices in mitigating climate-driven risks
Bibliographic record
Abstract
Introduction: Dengue fever poses a significant public health threat, particularly in tropical regions like Malaysia. The rising incidence of dengue outbreaks challenges healthcare systems and highlights the urgent need for effective preventive measures. Climate change, with rising temperatures and shifting rainfall patterns, is expected to worsen the dengue situation in the coming years. Methods: . This is a cross-sectional study conducted among adult residents of low-cost housing apartments in an urban poor community in Kuala Lumpur, Malaysia involving approximately 16,000 residents. A representative sample of 1,636 residents was calculated using the Krejcie and Morgan formula, and stratified random sampling was used to ensure proportional representation across the various floors of each PPR community apartment block. Data were collected using a structured questionnaire adapted from a validated Malay version. The questionnaire assessed respondents' knowledge, attitudes, and practices (KAP) related to dengue prevention, categorising KAP scores as "Good" (≥80%) or "Poor" (<80%). Descriptive statistics summarized population characteristics and KAP scores, while logistic regression identified predictors of KAP levels, with significance set at p ≤ 0.05. Results: In this community, 76.7% of participants exhibited poor knowledge and 83.1% had a negative attitude towards dengue, despite 66.7% demonstrating good preventive practices. The PPR location significantly predicts dengue knowledge, attitudes, and preventive practices, with p-values of less than 0.001 for all domains. Marital status also predicts dengue knowledge (p = 0.007) and preventive practices (p = 0.023), while prior infection with dengue is a predictor of preventive practices (p = 0.047). Conclusion: Despite the community's good dengue prevention practices, likely influenced by environmental expectations, there remains a critical need for education to sustain and strengthen these efforts, as climate change continues to worsen in the coming years. It is crucial to help residents grasp the relevance of these practices, so they can apply them more effectively as climate-driven risks intensify. Targeted interventions should de designed differently for each of the four PPR communities as their levels of knowledge, attitude and practices vary significantly, taking into account independent factors like marital status and prior dengue infection, which shape preventive behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".