Understanding dengue transmission by using participatory research and community-focused strategies for prevention and control in Bangladesh
Bibliographic record
Abstract
Globally, dengue has become one of the most alarming infectious diseases and its resurgence reflects the failure of traditional reductionistic disciplinary approach. Because neither an effective vaccine nor an effective vector control program is available for dengue prevention and control, a Community-Based Approach (CBA) to vector control and individual behavior change has been implemented in many countries. However, by and large, the CBA has failed as it ignored local community members’ basic needs and perspectives in these processes. By challenging reductionist notions, this research will delineate the mental maps of local urban residents of the City of Dhaka, Bangladesh concerning dengue transmission and methods of dengue prevention and control. This study involved focus group discussion in 3 wards of Dhaka City Corporation, semi-structured interview of 30 stakeholders representatives; 900 ward/community members (300 from each ward); 18 policy- and/or decision makers (national and local institutions) and community members’ mental map construction of 24 ward representatives (supplemented by 300 ward members). This study revealed the lack of intersectoral coordination between local and national institutions dealing with disease and household sanitation, and highlights the difficulties in avoiding dengue vectors in urban areas with irregular water supply, poor sanitation services, and finally the location of large and small construction zones all over the city. The conclusion emphasizes the importance of the knowledge about the daily problems faced by the community members and partnership needed in all sectors to address water supply problem and disease surveillance systems.
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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 | 0.028 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".