Preparedness and Disaster Response Training for Veterinary Students: Literature Review and Description of the North Carolina State University Credentialed Veterinary Responder Program
Bibliographic record
Abstract
The nation's veterinary colleges lack the curricula necessary to meet veterinary demands for animal/public health and emergency preparedness. To this end, the authors report a literature review summarizing training programs within human/veterinary medicine. In addition, the authors describe new competency-based Veterinary Credential Responder training at North Carolina State University College of Veterinary Medicine (NCSU CVM). From an evaluation of 257 PubMed-derived articles relating to veterinary/medical disaster training, 14 fulfilled all inclusion requirements (nine were veterinary oriented; five came from human medical programs). Few offered ideas on the core competencies required to produce disaster-planning and response professionals. The lack of published literature in this area points to a need for more formal discussion and research on core competencies. Non-veterinary articles emphasized learning objectives, commonly listing an incident command system, the National Incident Management System, teamwork, communications, and critical event management/problem solving. These learning objectives were accomplished either through short-course formats or via their integration into a larger curriculum. Formal disaster training in veterinary medicine mostly occurs within existing public health courses. Much of the literature focuses on changing academia to meet current and future needs in public/animal health disaster-preparedness and careers. The NCSU CVM program, in collaboration with North Carolina Department of Agriculture and Consumer Service, Emergency Programs and University of North Carolina at Chapel Hill School of Public Health, operates as a stand-alone third-year two-week core-curriculum training program that combines lecture, online, experiential, and group exercises to meet entry-level federal credentialing requirements. The authors report here its content, outcomes, and future development plans.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".