Dengue Disease Risk Mental Models in the City of Dhaka, Bangladesh: Juxtapositions and Gaps Between the Public and Experts
Bibliographic record
Abstract
Worldwide, more than 50 million cases of dengue fever are reported every year in at least 124 countries, and it is estimated that approximately 2.5 billion people are at risk for dengue infection. In Bangladesh, the recurrence of dengue has become a growing public health threat. Notably, knowledge and perceptions of dengue disease risk, particularly among the public, are not well understood. Recognizing the importance of assessing risk perception, we adopted a comparative approach to examine a generic methodology to assess diverse sets of beliefs related to dengue disease risk. Our study mapped existing knowledge structures regarding the risk associated with dengue virus, its vector (Aedes mosquitoes), water container use, and human activities in the city of Dhaka, Bangladesh. "Public mental models" were developed from interviews and focus group discussions with diverse community groups; "expert mental models" were formulated based on open-ended discussions with experts in the pertinent fields. A comparative assessment of the public's and experts' knowledge and perception of dengue disease risk has revealed significant gaps in the perception of: (a) disease risk indicators and measurements; (b) disease severity; (c) control of disease spread; and (d) the institutions responsible for intervention. This assessment further identifies misconceptions in public perception regarding: (a) causes of dengue disease; (b) dengue disease symptoms; (c) dengue disease severity; (d) dengue vector ecology; and (e) dengue disease transmission. Based on these results, recommendations are put forward for improving communication of dengue risk and practicing local community engagement and knowledge enhancement in Bangladesh.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".