A Survey of Veterinary Student and Veterinarian Perceptions of Shelter Medicine Employment
Bibliographic record
Abstract
Abstract Shelter medicine appears to be popular among prospective veterinarians, but there is a shortage of veterinarians entering the field to fill available positions. The reasons for this discordance are not well understand. This study describes veterinary students’ interest in shelter medicine, their perceptions of common duties, and their perceptions of employment attributes in shelter medicine, compared with those of current veterinarians. The sample included 146 first-year students, 155 final-year students, and 221 veterinarians who self-completed an online survey between September 2020 and March 2021. We found high levels of interest in shelter medicine, with 40% of first- and 43% of final-year students indicating they were likely to consider working in shelter medicine. Outreach clinics (84% of first-year students, 86% of final-year students), access-to-care clinics (82%, 83%), and loan forgiveness programs (75%, 64%) encouraged many veterinary students to consider working in shelter medicine. The risk of compassion fatigue, burnout, and stress (70%, 68%); weekend work (51%, 59%); euthanasia decision making (49%, 47%); euthanasia (43%, 41%); and expected salaries of shelter veterinarians (39%, 37%) acted as deterrents. Kruskal–Wallis H tests revealed students reported more positive ratings than veterinarians for most shelter medicine duties and employment characteristics, with moderate to strong consensus within groups. Little difference appeared between first- and final-year students. This study highlights target areas for animal shelters to boost recruitment of newly graduated veterinarians. Increasing veterinary students’ exposure to shelter medicine throughout their veterinary training may also help address their concerns regarding euthanasia, salary, and quality of care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".