Perception of Animal Welfare and Animal Abuse among Veterinary Students: Role of Individual and Sociodemographic Factors
Bibliographic record
Abstract
Animal welfare and animal abuse, although measurable, exhibit a high degree of subjectivity that conditions how they are perceived and the level of sensitivity. Both elements are influenced by individual and sociodemographic factors. To determine the perception of animal welfare among veterinary students and to study the main sociodemographic factors influencing the assessment of animal welfare. To evaluate animal welfare perception at the University of Las Palmas de Gran Canaria's Faculty of Veterinary Medicine, a 20-question survey was deployed via the Google Surveys platform. Distributed across all academic years, it was facilitated with QR codes located within the faculty premises. The data collection occurred from November 1, 2022, to November 30, 2022. A total of 223 students responded the questionnaire about perception of animal abuse, which represents 56.3% of the total enrollment in the academic year 2022-2023. Sensitivity to animal welfare, including academic training on how to respond to animal abuse, increased as students progressed through their studies. However, as students approached the end of their studies, they became less willing to make voluntary efforts. The profile of the veterinary student least sensitive to animal abuse appeared to be men without dogs who reside in rural habitats and have family members involved in hunting or fishing. We propose the implementation of intensive courses on animal welfare throughout the veterinary curriculum, along with an understanding of the veterinarian's role in reporting animal abuse. This approach aims to foster a foundation of critical awareness and commitment to animals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".