Veterinary Student Stress, the Benefit of Canine Pets, and Potential Institutional Support of Dog Ownership
Bibliographic record
Abstract
The stress of veterinary school and the high prevalence of psychological distress among veterinary students have been well documented. Pet ownership is known to improve overall health and reduce stress among the public. Yet, for veterinary students, owning a pet (especially a dog) can offer both rewards and challenges. The academic schedule for veterinary students often comprises long hours that can make caring for a dog challenging. This study explores the area of veterinary students’ dog care options and perspectives by examining two aspects of this issue: (a) currently available options, as reported by academic administrators, and (b) perceived need for these options, as reported by veterinary students. A survey of associate deans for academic affairs ( n = 30) found that routine on-site kenneling options for student-owned dogs are available at eight (26.6%) veterinary schools. Simultaneously, results of a student survey ( n = 768) revealed a great desire for on-campus services. Among students who did not have access to on-campus kenneling facilities, 71.5% (453 of 634) felt that creating these options would be important or very important. Across all students surveyed, 76% (581 of 764) felt it would be important to have on-site dog housing/care available. Students experience considerable stress over having to find accommodations or care for their dogs while engaged in academic activities. Thus, providing on-site boarding and care options for student-owned dogs can play an important role in both recruiting prospective veterinary students and enhancing the well-being of those currently in the program.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".