Professional Veterinary Programs' Perceptions and Experiences Pertaining to Emotional Support Animals and Service Animals, and Recommendations for Policy Development
Bibliographic record
Abstract
Given the unique nature of programs in professional veterinary medicine (PVM), the increasing numbers of students requesting accommodations for emotional support animals (ESAs) in higher education settings is of growing interest to student affairs and administrative staff in PVM settings. Since the legislation pertaining to this type of support animal differs from the laws governing disability service animals, colleges and universities now need to develop new policies and guidelines. Representatives from a sample of 28 PVM programs completed a survey about the prevalence of student requests for ESAs and service animals. PVM associate deans for academic affairs also reported their perceptions of this issue and the challenges these requests might pose within veterinary teaching laboratories and patient treatment areas. Responses indicated that approximately one third of PVM programs have received requests for ESAs (32.1%) in the last 2 years, 17.9% have had requests for psychiatric service animals, and 17.9% for other types of service animals. Despite this, most associate deans reported not having or not being aware of university or college policies pertaining to these issues. Most associate deans are interested in learning more about this topic. This paper provides general recommendations for establishing university or PVM program policies.
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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 | 0.032 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".