Ecohealth research in Southeast Asia: past, present and the way forward
Bibliographic record
Abstract
Ecohealth is a comprehensive approach to understanding health at its human, animal and environmental interface in a socio-ecological systems context. This approach was introduced widely in Southeast Asia (SEA) by the Canadian International Development Research Centre (IDRC) in the late 2000s. Aimed at addressing the problem of emerging infectious diseases (EIDs), numerous such projects and activities have been generated throughout the region. Ecohealth is increasingly converging with the One Health approach, as both movements emphasise a holistic understanding to health. We conducted a scoping review by considering all of the Ecohealth programmes, initiatives and projects that have been implemented in SEA since the introduction of the approach, and also gathered information from peer-reviewed literature. The objective of this paper is to review Ecohealth activities within SEA over the last 10 years to address the lessons learned, challenges faced and the way forward for Ecohealth in the region. Activities range from those focusing purely on capacity, projects focusing on research and projects covering both. Achievements to date include, for example, research contributing to the field of infectious diseases in relation to social ecological factors and associated urbanisation and agricultural intensification. Challenges remain at the project design and implementation level, in the available capacity and coordination to develop Ecohealth research teams in the countries, gauging teams' assimilation of Ecohealth's underlying tenets and their translation into sustainable disease prevention and control, as well as in the ability to scale up Ecohealth projects. We suggest that the way forward for Ecohealth should be from a regional perspective in terms of research, training and policy translation using Ecohealth in combination with the One Health approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".