Assessing Disaster Preparedness and Educational Needs of Private Veterinary Practitioners in Mississippi
Bibliographic record
Abstract
The veterinary medical education system faces increasing challenges in educating students in the most current technologies while responding to changing community needs and expectations. Communities expect veterinarians to be involved in disaster management at some level. The purpose of this study was to describe the level of disaster preparedness and educational needs of veterinary practitioners in Mississippi. A survey was mailed to 706 practitioners to assess disaster plans, disaster training, and familiarity with disaster-related organizations. Forty-three percent of veterinarians had a clinic disaster plan. Veterinary practitioners who had experienced a disaster were more likely to have a personal plan (odds ratio [ OR] = 4.55, 95% confidence interval [CI] = 2.47–8.37) and a clinic plan ( OR = 4.11, 95% CI = 2.28–7.44) than those who had not. Veterinarians residing in Mississippi Gulf Coast counties were more likely to have a personal plan ( OR = 3.62, 95% CI = 1.54–8.72) and a clinic plan ( OR = 3.09, 95% CI = 1.35–7.21) than were those residing in other areas. Only 17% of veterinarians had assistance agreements with other practices, and few veterinarians indicated having disaster education materials available for their clients. Twenty percent of respondents indicated having obtained formal disaster training, and more than two-thirds of respondents were interested in receiving disaster training, mostly in the form of online delivery. Results suggest that private veterinary practitioners have the desire and need to obtain disaster education. Providing opportunities for both veterinarians and veterinary students to obtain education in disaster management will result in better overall community disaster preparedness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".