One Health literacy and governance of health leaders across three countries following the pandemic
Bibliographic record
Abstract
Abstract Background To address health threats and promoting health of all, One Health is an important approach. To foster the implementation of One Health, a framework proposed by Blankart et al. (2024) postulates a reinforcing relationship between One Health literacy and governance after being triggered by an external event. We build on this model and aim to explore how the postulated mechanism differs among health policy and practice leaders across the UK, Germany, and Switzerland after the external triggering event of the COVID-19 pandemic. Methods We developed an online survey instrument for each of the 5 elements of the framework and conducted it in April 2024. We targeted mid- and end-career leaders and senior experts from the Sciana network that reported about their personal experiences and perceived changes (scale from -5 to + 5). We performed descriptive and inferential statistics to analyse the data. The survey was complemented by structured interviews to gather contextual information. Results Leaders and experts (N = 30, response rate: 30%) increased their One Health literacy (mean: 2.2), increasingly engaged with One Health principles (mean: 1.0), which resulted in more calls for optimized One Health governance (mean: 2.4). The interviewees reported that they adopted and implemented a more consistent One Health governance (mean: 2.6), which led to more familiarity with One Health principles (mean: 2.3). According to the results, the average effect on One Health literacy and Governance was stronger in Germany, followed by Switzerland and the UK. Conclusions We have demonstrated that triggering events such as the COVID-19 pandemic have led to improvements in One Health literacy and subsequently One Health governance. We therefore suggest that the postulated framework holds in practice and have showed that there are important differences in uptake between leaders in the three countries. Further research may build on these findings and develop more effective health policy. Key messages • We have contributed to the understanding of how pandemics improve One Health literacy and subsequent One Health governance using the example through a survey of leaders in health policy and practice. • Based on the understanding of triggering events’ effect on One Health literacy and governance specific recommendations for the implementation of One Health in public health can be developed.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".