Veterinarians and Public Health: The Epidemic Intelligence Service of the Centers for Disease Control and Prevention, 1951–2002
Bibliographic record
Abstract
Public health affords important and exciting career opportunities for veterinarians. The Epidemic Intelligence Service Program (EIS) of the Centers for Disease Prevention and Control (CDC) is a two-year post-graduate program of service and on-the-job training for health professionals, including veterinarians, who are interested in careers in epidemiology and public health. EIS serves as a major point of entry into the public health arena. Veterinarians applying to the program must have a Master of Public Health or equivalent degree, or demonstrated public health experience or course work. EIS officers are assigned to positions at CDC headquarters or in state and local health departments. During two-year assignments, they are trained in applied epidemiology, biostatistics, conducting outbreak investigations, emergency preparedness and response, and scientific communications. They conduct epidemiologic outbreak and other investigations, perform applied research and public health surveillance, serve the epidemiologic needs of state health departments, present at scientific and medical conferences, publish in the scientific literature, and disseminate vital public health information to the media and the public. EIS officers apply their training and skills to actual public health problems and issues, establish mentorships with recognized experts from CDC and other national and international health agencies, and travel domestically and internationally. Since 1951, 195 veterinarians have graduated from the program and gone on to make substantial contributions to public health in positions with federal, state, or local governments, academia, industry, and non-governmental organizations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".