Use and acceptance of long lasting insecticidal net screens for dengue prevention in Acapulco, Guerrero, Mexico
Bibliographic record
Abstract
BACKGROUND: Dengue, recognized by the WHO as the most important mosquito-borne viral disease in the world, is a growing problem. Currently, the only effective way of preventing dengue is vector control. Standard methods have shown limited effect, and there have been calls to develop new integrated vector management approaches. One novel tool, protecting houses with long lasting insecticidal screens on doors and windows, is being trialled in a cluster randomised controlled trial by a joint UADY/WHO TDR/IDRC study in various districts of Acapulco, Mexico, with exceptionally high levels of crime and insecurity.This study investigated the community's perspectives of long lasting insecticidal screens on doors and windows in homes and in schools, in order to ascertain their acceptability, to identify challenges to further implementation and opportunities for future improvements. METHODS: This was a sequential mixed-methods study. The quantitative arm contained a satisfaction survey administered to 288 houses that had received the intervention examining their perspectives of both the intervention and dengue prevention in general. The qualitative arm consisted of Focus Group Discussions (FGDs) with those who had accepted the intervention and key informant interviews with: schoolteachers to discuss the use of the screens in schools, program staff, and community members who had refused the intervention. RESULTS: Overall satisfaction and acceptance of the screens was very high, with only some operational and technical complaints relating to screen fragility and the installation process. However, the wider social context of urban violence and insecurity was a major barrier to screen acceptance. Lack of information dissemination and community collaboration were identified as project weaknesses. CONCLUSIONS: The screens are widely accepted by the population, but the project implementation could be improved by reassuring the community of its legitimacy in the context of insecurity. More community engagement and better information sharing structures are needed.The screens could be a major new dengue prevention tool suitable for widespread use, if further research supports their entomological and epidemiological effectiveness and their acceptability in different social and environmental contexts. Further research is needed looking at the impact of insecurity of dengue prevention programmes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".