Adoption of community-based strategies for sustainable vector control and prevention
Bibliographic record
Abstract
Community engagement strategies provide tools for sustainable vector-borne disease control. A previous cluster randomized control trial engaged nine intervention communities in seven participatory activities to promote management of the domestic and peri-domestic environment to reduce risk factors for vector-borne Chagas disease. This study aims to assess the adoption of this innovative community-based strategy, which included chickens' management, indoor cleaning practices, and domestic rodent infestation control, using concepts from the Diffusion of Innovations Theory. We used questionnaires and semi-structured interviews to understand perceptions of knowledge gained, intervention adoption level, innovation attributes, and limiting or facilitating factors for adoption. The analysis process focused on five innovation attributes proposed by the Diffusion of Innovations Theory: relative advantage, compatibility, complexity, trialability, and observability. Rodent management was highly adopted by participants, as it had a relative advantage regarding the use of poison and was compatible with local practices. The higher complexity was reduced by offering several types of trapping systems and having practical workshops allowed trialability. Observability was limited because the traps were indoors, but information and traps were shared with neighbors. Chicken management was not as widely adopted due to the higher complexity of the method, and lower compatibility with local practices. Using the concepts proposed by the Diffusion of Innovations Theory helped us to identify the enablers and constraints in the implementation of the Chagas vector control strategy. Based on this experience, community engagement and intersectoral collaboration improve the acceptance and adoption of novel and integrated strategies to improve the prevention and control of neglected diseases.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".