The Role of Veterinary Education in Safety Policies for Animal-Assisted Therapy and Activities in Hospitals and Nursing Homes
Bibliographic record
Abstract
Animal-assisted activities (AAA) and animal-assisted therapy (AAT) programs are increasing in popularity, but current programs vary in their safety and health policies. Veterinarians can have an important role in ensuring the safety of both the animals and humans involved, but it is unclear how best to educate veterinary students to serve effectively in this role. Therefore, the goal of this study was to assess the knowledge gaps and perceptions of first-year veterinary students on health and safety aspects of AAA/AAT programs by administering a survey. This information could then guide future educational training in veterinary schools to address the knowledge gaps in this area. Formal education during the veterinary curriculum had not yet been provided to these students on AAA/AAT before the survey. Of 98 first-year veterinary students, 91 completed the survey. When asked about policies on visiting animals, 58% of students responded that nursing homes are required to have a policy and 67% responded that hospitals are required to have one. Three quarters of students reported that veterinarians, animal handlers, and facilities should share the responsibility for ensuring safe human-animal interaction in AAA/AAT programs. Most (82%) of the students responded that all or most national and local therapy animal groups prohibit animals that consume raw meat diets from participating in AAA/AAT programs. The results of this survey will help veterinary schools better identify knowledge gaps that can be addressed in veterinary curricula so future veterinarians will be equipped to provide appropriate public health information regarding AAA/AAT 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 | 0.013 | 0.038 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".