Educating Veterinarians for Careers in Free-Ranging Wildlife Medicine and Ecosystem Health
Bibliographic record
Abstract
In the last 10 years, the field of zoological medicine has seen an expansive broadening into the arenas of free-ranging wildlife, conservation medicine, and ecosystem health. During the spring/summer of 2005, we prepared and disseminated a survey designed to identify training and educational needs for individuals entering the wildlife medicine and ecosystem health fields. Our data revealed that few wildlife veterinarians believe that the training they received in veterinary school adequately prepared them to acquire and succeed in their field. Wildlife veterinarians and their employers ranked mentorship with an experienced wildlife veterinarian, training in leadership and communication, courses and externships in wildlife health, and additional formal training beyond the veterinary degree as important in preparation for success. Employers, wildlife veterinarians, and job seekers alike reported that understanding and maintaining ecosystem health is a key component of the wildlife veterinarian's job description, as it is critical to protecting animal health, including human health. Today's wildlife veterinarians are a new type of transdisciplinary professional; they practice medicine in their communities and hold titles in every level of government and academia. It is time that we integrate ecosystem health into our curricula to nurture and enhance an expansive way of looking at veterinary medicine and to ensure that veterinary graduates are prepared to excel in this new and complex world, in which the health of wildlife, domestic animals, and people are interdependent.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".