Views from chief veterinary officers about decision making during animal health emergencies: A qualitative analysis
Bibliographic record
Abstract
This study's objectives were to investigate how delegates of the World Organisation for Animal Health (WOAH), particularly chief veterinary officers (CVOs), make decisions requiring immediate action, having national/international impacts on animal health and welfare and/or human health, and how the process creates new policies and practices. We interviewed 33 CVOs from 6 continents, on their background, jurisdiction, institutional structures, and decision-making processes, including types of decisions made, who they consulted, information used (and wished they knew), political, public and other influences, and resultant policy changes. The CVOs also discussed surprising and challenging phenomena, and what they learned. Qualitative analysis was conducted on interview transcriptions. Most CVOs had similar decision-making processes. They followed established protocols and national legislation aligned with WOAH international standards, relying on multidisciplinary teams of experts in science, economics, policy, and law, and those with knowledge of local field conditions, for guidance. Insufficient information and conflict between scientific evidence and political/economic pressure were common themes. Although stressful, most CVOs were committed to their work and felt they made valuable contributions towards both animal and human health. The findings suggest that regardless of background or specific geographical context, CVOs follow established protocols and need to have the ability to make informed subjective judgements as part of their decision making. Thus, CVO qualifications include subject matter knowledge and specific leadership qualities, which need to be considered when making CVO appointments. Presently, veterinarians receive inadequate training on integrating evidence and other factors to make informed, 'good' decisions. This study's findings should be considered when developing the educational programs for veterinary students and established practitioners.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".