Justice Is the Missing Link in One Health: Results of a Mixed Methods Study in an Urban City State
Bibliographic record
Abstract
BACKGROUND: One Health (OH) is an interdisciplinary collaborative approach to human and animal health that aims to break down conventional research and policy 'silos'. OH has been used to develop strategies for zoonotic Emerging Infectious Diseases (EID). However, the ethical case for OH as an alternative to more traditional public health approaches is largely absent from the discourse. To study the ethics of OH, we examined perceptions of the human health and ecological priorities for the management of zoonotic EID in the Southeast Asia country of Singapore. METHODS: We conducted a mixed methods study using a modified Delphi technique with a panel of 32 opinion leaders and 11 semi-structured interviews with a sub-set of those experts in Singapore. Panellists rated concepts of OH and priorities for zoonotic EID preparedness planning using a series of scenarios developed through the study. Interview data were examined qualitatively using thematic analysis. FINDINGS: We found that panellists agreed that OH is a cross-disciplinary collaboration among the veterinary, medical, and ecological sciences, as well as relevant government agencies encompassing animal, human, and environmental health. Although human health was often framed as the most important priority in zoonotic EID planning, our qualitative analysis suggested that consideration of non-human animal health and welfare was also important for an effective and ethical response. The panellists also suggested that effective pandemic planning demands regional leadership and investment from wealthier countries to better enable international cooperation. CONCLUSION: We argue that EID planning under an OH approach would benefit greatly from an ethical ecological framework that accounts for justice in human, animal, and environmental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".