The ethical considerations of primordial pandemic prevention from a one health perspective
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has left a devastating global toll. As such, there is a strong impetus to prevent future global pandemics. Ethical considerations are an integral element of pandemic preparedness and response plans and should be incorporated into any pandemic prevention plan to explicitly examine the incorporated values from various stakeholders. Our study aims to determine the ethical considerations of primordial pandemic prevention from a One Health perspective. METHODS: This was a prospective Delphi consensus seeking-study. We aimed to recruit a purposive, globally representative sample of experts in the fields of public health ethics, One Health ethics, pandemic ethics and pandemic prevention. Two rounds were completed between November 2021, and January 2022. The first round consisted of open-ended questions to establish ethical considerations for primordial pandemic prevention. Thematic analysis was used to uncover themes. The second-round presented the ethical consideration results of the first round, and asked participants to rate the importance of each of them. RESULTS: The first-round had 27 participants, and the second-round had 25 participants. Both rounds had global representation from all intended fields of expertise. There were five ethical considerations for which consensus was achieved: Promoting equity, global collective effort, distributive justice, evidence-based efficiency and the interconnectedness of humans, animals and the environment. CONCLUSIONS: Our study identified five ethical considerations for primordial pandemic prevention from a globally representative sample. The findings will contribute to current and future pandemic prevention policy, and expand ethics research in the fields of One Health, pandemic prevention and zoonotic disease control.
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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 | 0.184 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.060 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.017 |
| 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".