Using a Standardized Client Encounter in the Veterinary Curriculum to Practice Veterinarian–Employer Discussions about Animal Cruelty Reporting
Bibliographic record
Abstract
Animal cruelty is the antithesis of animal welfare. Because veterinarians take an oath to protect animal welfare, they are professionally obligated to report animal cruelty. Several US states have mandatory reporting laws for veterinarians, and both the American Veterinary Medical Association (AVMA) and the American Animal Hospital Association support reporting. Some state veterinary practice acts, such as Arizona's, also require reporting. Despite this, animal cruelty is not always emphasized in veterinary curricula. As a result, not all veterinary students and graduates feel comfortable recognizing signs of animal cruelty and may not be aware of the resources that are available to them when considering reporting. AVMA suggests that practices develop their own protocols for identifying signs that patients may have been victims of cruelty and consulting on cases with senior colleagues with regard to when to report. To enhance student comfort with these conversations, Midwestern University College of Veterinary Medicine developed a standardized client encounter titled "Grizabella's Final Fight." I hope that other colleges of veterinary medicine will adapt this teaching tool to allow students the opportunity to practice discussions surrounding animal cruelty reporting in the context of state-specific legislation that guides their code of professional conduct.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".