Assessing Bioterrorism Preparedness and Response of Rural Veterinarians: Experiences and Training Needs
Bibliographic record
Abstract
Veterinarians play a unique role in emergency preparedness and response, and federal agencies and academic institutions therefore allocate considerable resources to provide training to enhance their readiness. However, the level of preparedness of veterinarians in many rural regions is yet to be improved. This article reports an assessment of the bioterrorism preparedness, specifically the experience and training needs, of rural veterinarians in North Texas. The study employed a cross-sectional design with a study population that included all veterinarians (N = 352) in the 37 counties within Texas Department of State Health Services Regions 2 and 3. Data on veterinarians practicing or residing in the target region were obtained from the Texas State Board of Veterinary Medical Examiners. The response rate was 35% (n = 121). Results indicate that chemical exposure was the condition most frequently seen and treated, followed by botulism and anthrax. The majority (80%) of respondents indicated that they had not previously participated in training related to bioterrorism preparedness, and many (41%) also indicated a willingness to participate in a state health department-initiated bioterrorism response plan. However, only 18% were confident in their ability to diagnose and treat bioterrorism cases. These results suggest that many North Texas veterinarians practicing in rural regions could benefit from additional training in bioterrorism preparedness and response. An area in particular need of further training is the diagnosis and treatment of Category A agents. Federal, state, and local health agencies are urged to increase training opportunities and to make additional efforts to involve veterinarians in bioterrorism preparedness and response.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".