Community perspectives on dengue transmission in the city of Dhaka, Bangladesh
Bibliographic record
Abstract
BACKGROUND: The recurrence of dengue has become a growing public health threat. This research examines the knowledge, beliefs, attitudes and practice of local community members regarding dengue transmission in the city of Dhaka, Bangladesh. It also investigates explanatory demographic and socioeconomic factors that affect community knowledge, beliefs and practices. METHODS: In July-August 2011, a random sample of household heads or alternatives (n=300) was surveyed in 12 wards of Dhaka. This survey was supplemented by 12 focus group discussions (n=107) and 18 key informant interviews in three selected wards. RESULTS: Most community members had heard about dengue (91.3%; 274/300) and knew (93.7%; 281/300) that mosquitoes act as the primary vector of its transmission. In contrast, most (87.3%; 262/300) was unaware that Aedes mosquitoes prefer to lay their eggs in water containers. Multivariate logistic regression modeling revealed that the respondents in age group 45-60 years were 2.83 times more likely to have positive attitudes towards undertaking precautionary measures to prevent dengue than the respondents aged <25 years. CONCLUSIONS: These findings confirm the presence in local communities of misconceptions and considerable knowledge gaps about dengue transmission that could be improved by formulating interventions targeting specific subgroups of the population.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".