Veterinary Medicine Educational Requirements to Meet the Needs of the US Agency for International Development
Bibliographic record
Abstract
The US Agency for International Development (USAID) works within the overall purpose of US foreign assistance to improve the lives of the citizens of the developing world. Through partnerships with other agencies, organizations, and governments, and using its field offices around the world, USAID strives to develop local capacity and thus build sustainable development. Two specific USAID programs pertinent to veterinary medicine are global health and agriculture. In the area of global health, veterinarians can aid USAID's work to improve the quality, availability, and use of essential health services that specifically target maternal and child health, HIV/AIDS, family planning and reproductive health, infectious diseases, environmental health, nutrition, and other life-saving areas. The challenge of making the agricultural sector in a developing country more productive is a critical one for USAID and a clear area for input from the veterinary profession. Animal agriculture is the largest single sector of agricultural economies in most developing countries, and livestock remains a critical component of poverty alleviation. There are educational requirements that benefit anyone working at USAID and can be met prior to admittance to a DVM program, as part of a DVM curriculum, or in post-graduate training/employment, such as proficiency in a foreign language; environmental sciences background; familiarity with accounting and management techniques; expertise in foreign animal diseases, zoonotic diseases, epidemiology, food safety, and nutrition, as well as the application to human health of those areas; an advanced degree such as an MPH; and management experience. Appropriately trained veterinarians can make enormous contributions to USAID's global efforts to improve the health and agriculture sectors of developing nations.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.057 |
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".