Enhancing Human–Animal Relationships through Veterinary Medical Instruction in Animal-Assisted Therapy and Animal-Assisted Activities
Bibliographic record
Abstract
Instruction in animal-assisted therapy (AAT) and animal-assisted activities (AAAs) teaches veterinary medical students to confidently and assertively maximize the benefits and minimize the risks of this union of animals and people. Instruction in AAT/AAA also addresses requirements by the American Veterinary Medical Association Council on Education that accredited schools/colleges of veterinary medicine include in their standard curriculum the topics of the human-animal bond, behavior, and the contributions of the veterinarian to the overall public and professional health care teams. Entry-level veterinarians should be prepared to: (1) assure that animals who provide AAT/AAA are healthy enough to visit nursing homes, hospitals, or other institutions; (2) promote behavior testing that selects animals who will feel safe, comfortable, and connected; (3) advise facilities regarding infection control and ways to provide a safe environment where the animals, their handlers, and the people being visited will not be injured or become ill; and (4) advocate for their patients and show compassion for their clients when animals are determined to be inappropriate participants in AAT/AAA programs. This article presents AAT/AAA terminology, ways in which veterinarians can advocate for AAT/AAA, the advantages of being involved in AAT/AAA, a model AAT/AAA practicum from Tuskegee University's School of Veterinary Medicine (TUSVM), and examples of co-curricular activities in AAT/AAA by TUSVM's student volunteers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".