Association among ecological and behavioural attributes, dengue vector and disease control: a cross-sectional study of the city of Dhaka, Bangladesh
Bibliographic record
Abstract
BACKGROUND: This study examines vector density, the prevailing knowledge, awareness, attitudes and practice (KAAP) of community members regarding dengue disease and their willingness to pay (WTP) for vector control in Dhaka, Bangladesh. METHODS: A population-based, cross-sectional study design was followed: (i) an entomological survey was carried out in 727 randomly selected households in 12 wards, representing four urban ecological zones and (ii) a survey of 330 household heads was conducted to study their KAAP. The χ2 test and multinomial logistic regression (MLR) were applied to investigate factors associated with WTP and other variables. RESULTS: The Stegomyia indices significantly vary among the urban zones, revealing that the paved and built areas with concentrated public/commercial services have the highest mosquito density. Most respondents (93.9%) knew about dengue and its severity (90.3%); however, many of them were unaware (79.3%) about the types of mosquitoes causing dengue. MLR modelling reveals that average spending per month for mosquito control, household income and knowledge about the effects of land use and seasonality on dengue were significantly associated with the WTP for controlling the dengue vector. CONCLUSIONS: Concerted efforts should be made to increase awareness about dengue transmission and develop community-based sustainable dengue vector control programmes involving both the public and private sectors.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".