EcoHealth research in Southeast Asia: Past, now, and the ways forward
Bibliographic record
Abstract
EcoHealth is one of the comprehensive concepts to look at health as an integrative component of the complex relation of human, animal and environment. Although it was introduced in South East Asia (SEA) late in 2000’s by IDRC, its development in the region shows a dynamics in the landscape of research and application of EcoHealth in various fields such as emerging and zoonotic diseases, agriculture and health, education and training. The objective of this presentation is to review EcoHealth activities in SEA of the last 10 years to address lessons learned, challenges and future of EcoHealth in the region. We analysed all the EcoHealth programmes, initiatives and projects (now called projects) that have been being implemented in the past 10 years with support of IDRC in SEA. Main considered EcoHealth projects are: APEIR (Asian Partnership on Emerging Infectious Diseases Research), EcoZD (Ecosystem Approaches to the Better Management of Zoonotic Emerging Infectious Diseases in SEA), EcoEID (EcoHealth emerging infectious diseases research in SEA), FBLI (Field Building Leadership Initiative in SEA), BECA (Building Capacity in EcoHealth for SEA). The level of Ecohealth characterised by how much “EcoHealth content” was analysed. The results showed that generally, EcoHealth has been well perceived and taken by partners, in particular academia, policy makers and communities and generated some good research results in the field of ZEIDs. Some projects focused purely on capacity, others on research or both. However, the challenges remain at the project design and implementation level but also on the available capacity and coordination to develop EcoHealth research and teams in the countries as well as the issue of EcoHealth scaling-up. Finally we will present the ways forward of EcoHealth from a regional perspective in terms of research, training and policy translation using EcoHealth in combination with One Health approach.
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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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".