Public awareness of One Health in China
Bibliographic record
Abstract
One Health is recognized as an increasingly important approach to global health. It has the potential to inform interventions and governance approaches to prevent future pandemics. Successfully implementing the One Health approach in policy will require active engagement from the public, which begs the question: how aware is the public of One Health? In this study, we examine the level and distribution of One Health awareness among the general public in China using a survey conducted in Beijing (n = 1820). We distinguish between awareness of the term of “One Health” versus awareness of the core set of ideas – the interconnection between the health of people, animals, and the environment. Our analysis shows that 40% of respondents reported that they have heard of the term, but more than double the number indicated that they recognize the core idea of interconnection between people, animals, and the environment. Specifically, about 83% of the respondents said that they believe people's health is closely connected to animal health and 86% believe people's health is closely connected to plant and environmental health. Multiple regression analysis indicates that women, younger people, and individuals with a higher level of education show higher levels of One Health awareness than their counterparts. Being aware of the term is associated with higher recognition of the core ideas. Policymakers and health practitioners should consider these findings when designing public awareness campaigns and educational initiatives to promote One Health principles.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".