Theory of Change for Building Stronger Wildlife Health Surveillance Systems Globally
Bibliographic record
Abstract
Emerging and re-emerging infectious diseases affecting wildlife have highlighted the need for wildlife health surveillance (WHS), given the interconnected role of wildlife in maintaining the health of natural resources, agriculture, and human populations. Although global policies and guidelines exist, a key gap remains in the implementation of WHS systems at local and national levels. A working group of local, national, and global stakeholders in WHS was established to address this gap. In this Viewpoint, we report a theory of change (ToC) developed to support implementation of WHS from local to global scales. Using established methods for developing a collaborative ToC, we leveraged our expertise and identified six transformative pathways to be implemented via collaborations across scales and contexts: mindset change, policy and investment, evidence-based practice, user-driven technologies, capacity enhancement, and mobilisation of a global community of practice. This ToC serves as a roadmap for the development of effective WHS systems that support adaptive management and implementation. WHS is fundamental to understanding the impact of health threats on biodiversity, domestic animals, and humans. This ToC also presents an approach to operationalise the integration of wildlife health into collaborative One Health surveillance systems.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".