Épidémies récurrentes de la dengue au Burkina Faso : préférences communautaires pour une intervention de prévention de la maladie
Bibliographic record
Abstract
BACKGROUND: Community-based interventions have proven effective in several Latin American countries in controlling dengue vector Aedes aegypti and reducing the burden of the disease. However, we did not find any study reporting the assessment or implementation of such interventions in Sub-Saharan Africa. This article presents local communities' preferences for activities as part of the implementation of a community-based intervention for dengue prevention in Ouagadougou (Burkina Faso) where dengue epidemics are recurrent during the rainy season. METHODS: A mixed-method study combining qualitative and quantitative data collection was conducted. Information from 983 households and their preferences for community-based activities for dengue prevention were collected in five neighborhoods of the city using a quantitative questionnaire. Then, 15 qualitative focus groups were organized in one of the neighborhoods that was randomly selected to receive a community-based intervention for dengue prevention. These groups were made up of 216 people representing the different socio-cultural categories: community leaders, men, women, young girls and boys. RESULTS: More than 95% of household respondents to the quantitative questionnaire found community-based interventions acceptable and/or useful: to raise awareness of mosquito-borne disease transmission, to identify and remove the mosquito breeding sites and areas favorable to the development of the adult vectors. Most participants in the focus groups, preferred outreach activities such as video/debate sessions, school and home education sessions, focus groups. They also preferred the implementation of community working groups, responsible for identifying and eliminating mosquito breeding sites in the neighborhood. However, many participants had reservations about sending preventive text messages to residents. They found it feasible but not useful since most people cannot read. CONCLUSION: This study shows that it is important to get the local communities involved in the formulation of health prevention activities in sub-Saharan Africa where some interventions are often implemented using strategies from other continents.
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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 | 0.008 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".