Evolution and expansion of the One Health approach to promote sustainable and resilient health and well-being: A call to action
Bibliographic record
Abstract
One Health is a transdisciplinary approach used to address complex concerns related to human, animal, plant, and ecosystem health. One Health frameworks and operational tools are available to support countries and communities, particularly for the prevention and control of zoonotic diseases and antimicrobial resistance and the protection of food safety. However, One Health has yet to be implemented in a manner that fully considers the complexities and interconnectedness of the diverse influences that have impacts at a larger system level. This lack of consideration can undermine the sustainability of any positive outcomes. To ensure the One Health approach can function effectively within the new global context of converging and escalating health, social, economic, and ecological crises, it must evolve and expand in three overlapping dimensions: (1) Scope: the partners, knowledge, and knowledge systems included, (2) Approach: the techniques, methodologies, and scholarship considered, and (3) Worldview inclusivity: the interweaving of other worldviews together with the mainstream scientific worldview that currently predominates. Diverse partners and knowledge from outside the mainstream health and scientific sectors, including Indigenous peoples and representatives of local communities, and traditionally generated knowledge, must be included. These systems of knowledge can then be braided together with mainstream science to comprise a holistic framework for decision-making. Scholarship and methodologies being applied in other fields and contexts to solve complex challenges and manage uncertainty, such as collaborative governance, social-ecologic systems theory, and complexity science, must be recognized and incorporated. The spectrum of considered worldviews must also expand to authentically integrate the expanded scope and approach into action and sustainable impact. By increasing community and social engagement and by recognizing and entwining different worldviews, the plurality of disciplines, and traditional and scientific ways of knowing to address community concerns in the contexts in which they exist, we can ensure that One Health remains effective and true to its paradigm in our rapidly changing and complex world.
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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 | 0.088 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.109 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 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".