The Importance of Animal Welfare Science and Ethics to Veterinary Students in Australia and New Zealand
Bibliographic record
Abstract
The study of animal welfare and ethics (AWE) as part of veterinary education is important due to increasing community concerns and expectations about this topic, global pressures regarding food security, and the requirements of veterinary accreditation, especially with respect to Day One Competences. To address several key questions regarding the attitudes to AWE of veterinary students in Australia and New Zealand (NZ), the authors surveyed the 2014 cohort of these students. The survey aimed (1) to reveal what AWE topics veterinary students in Australia and NZ consider important as Day One Competences, and (2) to ascertain how these priorities align with existing research on how concern for AWE relates to gender and stage of study. Students identified triage and professional ethics as the most important Day One Competences in AWE. Students ranked an understanding of triage as increasingly important as they progressed through their program. Professional ethics was rated more important by early and mid-stage students than by senior students. Understanding the development of animal welfare science and perspectives on animal welfare were rated as being of little importance to veterinary graduates as Day One Competences, and an understanding of "why animal welfare matters" declined as the students progressed through the program. Combined, these findings suggest that veterinary students consider it more important to have the necessary practical skills and knowledge to function as a veterinarian on their first day in practice.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".