Assessment of Emergency Preparedness of Veterinarians in New York
Bibliographic record
Abstract
Veterinarians have unique skills and abilities that could be useful in preparing for and responding to disasters and emergencies. However, veterinarians are often absent from emergency preparedness planning and exercises, and little is known about veterinarians' perceptions of emergency preparedness. A focus group was conducted among veterinarians to explore issues such as previous emergency-preparedness education, types of training needed, barriers to participation in training, and future steps to overcome identified barriers. Focus-group participants reported that they had had little to no emergency-preparedness training and had no clear understanding of what their specific role should be in an emergency. Participants also reported several barriers to participation in training and expressed significant concerns about their ability to respond in an emergency. The concerns reported include limited knowledge of zoonotic diseases, confusion about providing care for animals displaced during natural disasters, and poor relationships with other health professions. In order to respond to disasters, veterinarians require training tailored to their concerns and needs. Furthermore, partnerships between veterinarians and health care workers need to be further developed and strengthened.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".