An innovative ecohealth intervention for Chagas disease vector control in Yucatan, Mexico
Bibliographic record
Abstract
BACKGROUND: Non-domiciliated (intrusive) triatomine vectors remain a challenge for the sustainability of Chagas disease vector control as these triatomines are able to transiently (re-)infest houses. One of the best-characterized examples is Triatoma dimidiata from the Yucatan peninsula, Mexico, where adult insects seasonally infest houses between March and July. METHODS: We focused our study on three rural villages in the state of Yucatan, Mexico, in which we performed a situation analysis as a first step before the implementation of an ecohealth (ecosystem approach to health) vector control intervention. RESULTS: The identification of the key determinants affecting the transient invasion of human dwellings by T. dimidiata was performed by exploring associations between bug presence and qualitative and quantitative variables describing the ecological, biological and social context of the communities. We then used a participatory action research approach for implementation and evaluation of a control strategy based on window insect screens to reduce house infestation by T. dimidiata. CONCLUSIONS: This ecohealth approach may represent a valuable alternative to vertically-organized insecticide spraying. Further evaluation may confirm that it is sustainable and provides effective control (in the sense of limiting infestation of human dwellings and vector/human contacts) of intrusive triatomines in the region.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".