Increasing awareness of zoonotic diseases among health workers and rural communities in Southeast Asia
Bibliographic record
Abstract
Public awareness and education efforts can help in tackling zoonoses.For the past five years, EcoZD (see definitions), an action research project on zoonotic diseases has been working in six countries in Southeast Asia.Each country team consisted of local individuals and institutions with knowledge of Ecohealth (see definitions), representing multiple disciplines carrying out research on zoonotic emerging infectious diseases.In a number of countries, the teams started by evaluating the familiarity of local communities, health workers and occupational groups with zoonoses.This brief highlights what the teams learned about risky behaviours and practices in local communities and strategies they developed to raise awareness.EcoZD, also known as the Ecosystem Approaches to the Better Management of Zoonotic Emerging Infectious Diseases in Southeast Asia project was an initiative funded by the International Development Research Centre (IDRC) and coordinated by the International Livestock Research Institute (ILRI).The project worked in Cambodia, China, Indonesia, Laos, Thailand and Vietnam.Ecohealth is an approach that recognizes there are links between humans and their biophysical, social and economic environments that are reflected in an individual's health.Ecohealth brings together physicians, veterinarians, ecologists, economists, social scientists, planners and others to understand how ecosystem changes are negatively impacting human health and to provide practical solutions to reduce the negative health impacts of ecosystem change.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".