A social-ecological analysis of community perceptions of dengue fever and Aedes aegypti in Machala, Ecuador
Bibliographic record
Abstract
The growing burden of dengue fever and the lack of a vaccine or specific medical treatment have increased the urgency of the public health sector to identify alternative management strategies. A prevailing trend in Latin America has been a shift towards decentralized vector control programs with integrated management strategies, requiring significant intersectoral coordination, community engagement, and knowledge of the local social-ecological system (SES). Community perceptions and responses are a critical component of this system, since perceptions shape actions, and thus govern behavioral responses and acceptance of shifts in policy and management.We investigated perceptions, misconceptions, and local SES risk factors for dengue in high risk communities located at the urban periphery and center in Machala, Ecuador. We facilitated twelve focus group discussions with community members using semi-structured question guides and causal diagrams. Focus groups were recorded, transcribed, and coded to identify emergent themes using qualitative methods for theme analysis. To estimate the relative importance of the themes in each study area, we tabulated the number of focus groups in which each theme was present. Household surveys (n = 79) were conducted to further explore these themes, and we compared survey responses from the two areas using descriptive statistics.We identified thirty biophysical, political-institutional, and community-household risk factors for dengue. People at the periphery identified a greater number of risk factors. Dengue control required considerable investment of time and resources, which presented a greater challenge for women and people at the periphery. Common misperceptions included confusion with other febrile diseases, lack of knowledge of transmission mechanisms, and misconceptions about mosquito behavior. People perceived that dengue control programs had been limited by the lack of inter-institutional coordination and lack of social cohesion.There is a need for local, policy-relevant research that can be translated to strengthen the design, implementation, and evaluation of new dengue management strategies. This study contributes to a growing body of research in this area. Based on these findings, we identify key policy and management recommendations that will inform the ongoing transition to a decentralized dengue control program in Ecuador and other dengue endemic countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".