Assessing Veterinary Practice and Practitioner Preparedness for Natural and Man-Made Disasters, Including COVID-19
Bibliographic record
Abstract
Natural and man-made disasters lead to hundreds of millions of dollars in economic losses annually worldwide. Veterinarians are most qualified to support local, state, national, and international efforts in emergency management. However, they may lack the knowledge and advanced training to most effectively plan, prepare, and respond. Currently, only two colleges offer training embedded in their core veterinary curriculum. In this study, a survey was conducted to gain an understanding of veterinary practice and practitioner preparedness for natural and man-made disasters in the United States and Canada, with questions assessing pandemic preparedness. The participants graduated from 28 American Veterinary Medical Association (AVMA)-accredited veterinary colleges globally and 2 non-accredited veterinary colleges, represent a diverse set of veterinary practice types, and have an average of 26 years' practice experience. Overall, 63.5% of veterinary respondents had experienced a natural disaster, while only 9.6% had experienced a man-made disaster. Approximately 66% reported having a practice disaster preparedness plan, while less than 20% of those actively maintained and updated the plan. Furthermore, less than 50% of the practices and practitioners were ready to face the challenges of a global pandemic. Approximately 68% reported using some form of communication to educate clients about family and pet disaster readiness. Many felt that some advanced disaster readiness training would have been helpful in their veterinary curriculum. Our findings indicate that additional training in the veterinary curriculum, as well as continuing education, would help veterinarians and practices be better prepared for natural and man-made disasters.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".