Fat Dogs and Coughing Horses: K–12 Programming for Veterinary Workforce Development
Bibliographic record
Abstract
Workforce development strategies to educate, inform, and diversify the veterinary profession of the future must begin with children in elementary school. This article provides a description of the Fat Dogs and Coughing Horses program, which takes a multifaceted approach toward informing young students, beginning in first grade, about the interesting work and career opportunities available in the field of veterinary medicine. The program, a collaboration among Purdue University and Indiana public schools, is supported by a Science Education Partnership Award from the Office of Research Infrastructure Programs, a component of the National Institutes of Health. The overall goal of the program is to provide formal and informal educational opportunities for students, parents, teachers, and the public about the science involved in keeping people and their animals healthy. Examples of health concerns that impact both people and their pets are used to inform and excite children about careers in the health sciences. The program resulted in (1) curricula for students in Grades 1-3, 6, and 9; (2) four children's books and a set of collectible cards which highlight veterinarians, veterinary technicians, and research scientists who work with animals; and (3) four traveling museum-level quality exhibits. Preliminary assessment data has shown that the implementation of the curricula enhanced student science learning and science attitudes and interests. The program provides evidence that partnerships among professionals in veterinary medicine and K-12 education can result in impactful workforce development programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".