Career Preferences and Opinions on Animal Welfare and Ethics: A Survey of Veterinary Students in Australia and New Zealand
Bibliographic record
Abstract
Historically, the veterinary profession has understood animal welfare primarily in terms of animal health and productivity, with less recognition of animals' feelings and mental state. Veterinary students' career preferences and attitudes to animal welfare have been the focus of several international studies. As part of a survey in Australia and New Zealand, this study reports on whether veterinary students prioritize animal welfare topics or professional conduct on the first day of practice and examines links between students' career preferences and their institution, gender, and year of study. The questionnaire was designed to explore the importance that students assign to topics in animal welfare and ethics. Of the 3,320 students invited to participate in the online survey, a total of 851 students participated, representing a response rate of 25.5%. Students' preferences increased for companion-animal practice and decreased for production-animal practice as they progressed through their studies. Females ranked the importance of animal welfare topics higher than males, but the perceived importance declined for both genders in their senior years. In line with previous studies, this report highlighted two concerns: (1) the importance assigned to animal welfare declined as students progressed through their studies, and (2) males placed less importance overall on animal welfare than females. Given that veterinarians have a strong social influence on animal issues, there is an opportunity, through enhanced education in animal welfare, to improve student concern for animal welfare and in turn improve animal care and policy making by future veterinarians.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".