The Role of Sub-National Leaders Implementing the One Health Approach
Bibliographic record
Abstract
Successful public health interventions using the One Health (OH) approach require the broad committed collaboration of individuals, institutions and technical and policy organizations from all sectors of society. In many communities, city and district mayors (sub-national leaders) directly control the administrative functions that serve the well-being of their constituents and are important stakeholders in setting and implementing priorities and mobilizing local capacities, resources and concrete action. A multisectoral and transdisciplinary team composed of the Indonesian Ministries of Health, Home and Agriculture and the Association of All Health Offices; the International Union Against Tuberculosis and Lung Disease (The Union); the University of Western Ontario, Canada and the World Organization for Animal Health (WOAH) convinced this established consortium of mayors, the Asia Pacific Cities Alliance for Health and Development (APCAT), to join forces and adopt and implement a One Health workshop with fellow Asia Pacific regional city and district mayors and decision-makers in preparation for the November 2022 G20 meeting in Indonesia. The main objective of this workshop was to highlight the pivotal, practical role mayors play in advocacy, action and accountability for current and future diseases at the human-animal-environment interface using the OH approach. The workshop was conducted on 2 June, 2022, with 2544 (1577 on zoom and 967 on YouTube) people attending this virtual event from 16 countries and 431 cities. An overwhelming majority of participants agreed that the OH approach can prevent outbreaks of zoonotic diseases. The mayors committed by signing a political declaration that addressed local challenges through the implementation of local strategies based on international standards using the OH approach. Activities were formulated and initiated to prevent the risks of future disease outbreaks, epidemics and pandemics caused by zoonotic diseases.
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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 | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".