Academic Food-Supply Veterinarians: Future Demand and Likely Shortages
Bibliographic record
Abstract
The future demand for and potential shortages of food-supply veterinarians have been the subject of much concern. Using the Delphi forecasting method in a three-phase Web-based survey process, a panel of experts identified the trends and issues shaping the demand for and supply of academic food-animal veterinarians, then forecasted the likely future demand and shortages of food-supply veterinarians employed in academic institutions in the United States and Canada through 2016. The results indicate that there will be increasing future demand and persistent shortages of academic food-supply veterinarians unless current trends are countered with targeted, strategic action. The Delphi panel also evaluated the effectiveness of several strategies for reversing current trends and increasing the number of food-supply veterinarians entering into academic careers. Academic food-supply veterinarians are a key link in the system that produces food-supply veterinarians for all sectors (private practice, government service, etc.); shortages in the academic sector will amplify shortages wherever food-supply veterinarians are needed. Even fairly small shortages have significant public-health, food-safety, animal-welfare, and bio-security implications. Recent events demonstrate that in an increasingly interconnected global economic food supply system, national economies and public health are at risk unless an adequate supply of appropriately trained food-supply veterinarians is available to counter a wide variety of threats ranging from animal and zoonotic diseases to bioterrorism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".