COVID-19 and zoonoses in Brazil: Environmental scan of one health preparedness and response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emergence of the COVID-19 pandemic reinforced the central role of the One Health (OH) approach, as a multisectoral and multidisciplinary perspective, to tackle health threats at the human-animal-environment interface. This study assessed Brazilian preparedness and response to COVID-19 and zoonoses with a focus on the OH approach and equity dimensions. We conducted an environmental scan using a protocol developed as part of a multi-country study. The article selection process resulted in 45 documents: 79 files and 112 references on OH; 41 files and 81 references on equity. The OH and equity aspects are poorly represented in the official documents regarding the COVID-19 response, either at the federal and state levels. Brazil has a governance infrastructure that allows for the response to infectious diseases, including zoonoses, as well as the fight against antimicrobial resistance through the OH approach. However, the response to the pandemic did not fully utilize the resources of the Brazilian state, due to the lack of central coordination and articulation among the sectors involved. Brazil is considered an area of high risk for emergence of zoonoses mainly due to climate change, large-scale deforestation and urbanization, high wildlife biodiversity, wide dry frontier, and poor control of wild animals' traffic. Therefore, encouraging existing mechanisms for collaboration across sectors and disciplines, with the inclusion of vulnerable populations, is required for making a multisectoral OH approach successful in the country.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it