Integrating Animal Welfare into Veterinary Education: Using an Online, Interactive Course
Bibliographic record
Abstract
Veterinarians in the United States and abroad are faced with growing public concern for the welfare of animals, particularly those in production. To prepare veterinarians to exert the leadership expected by the public and industry, steps should be taken to provide instruction in animal welfare at veterinary colleges. The ultimate goal is to offer courses in animal welfare in a consistent manner on a global scale, utilizing existing expertise in an efficient and cost-effective manner. Given the intense curricula of veterinary schools and the scarcity of instructors trained in animal welfare, a nontraditional approach is needed to educate veterinary students in the United States and abroad in animal welfare. Michigan State University (MSU) is developing a graduate-level, online interactive course in animal welfare assessment. The course will approach the topic of animal welfare education from a holistic, multidisciplinary standpoint (encompassing ethics, economics, and behavior) and address issues important to the general public and the international community. The MSU course will draw on renowned international animal welfare experts, allowing students to receive high-quality education that would be difficult in any other circumstance. The course will bridge an important gap in the veterinary curriculum and offer a complete and congruous education in animal welfare to veterinarians worldwide. The MSU course will also serve as a model for collaboration in content assembly and course delivery, by using technology to leverage global expertise in the interests of educational equity. In addition to innovative technology, such as the use of Web-collaboration software to create the course, a variety of media that enable students to interact with the material will also be incorporated throughout the course. Thus, the course will not only utilize the Internet to provide access to high-quality material, but will also require the active participation of the student, which is needed for effective learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".