Health and sustainable development: challenges and opportunities of ecosystem approaches in the prevention and control of dengue and Chagas disease
Bibliographic record
Abstract
A world of healthy people living in healthy ecosystems has proven to be an elusive goal of the sustainable development agenda. Numerous science-based assessments agree on the fundamental interdependence between people's health, the economy, and the environment, and on the urgency for more determined and concerted action based on multi-sector participatory approaches at the global and local levels. For knowledge to be policy-relevant and capable of contributing to healthy and sustainable development, it must take into account the dynamic and complex interactions between ecological and social systems (systems thinking), and it must be linked to development actions. This in turn requires greater interaction and exchange between decision-makers, researchers and civil society (a multi-stakeholder participatory process); and the harnessing of different disciplines and of different kinds of knowledge (a transdisciplinary approach). Ecosystem approaches to human health (ecohealth) link these elements in an adaptable framework for research and action. This paper presents an overview of ecohealth research approaches applied to vector-borne diseases, with particular attention to multi-stakeholder participation given its prominence in the sustainable development policy discourse.
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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 | 0.020 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".