A Survey of Veterinary Medical Professionals’ Knowledge, Attitudes, and Experiences with Animal Sexual Abuse
Bibliographic record
Abstract
There has been little study of animal sexual abuse (ASA). Subsequently, little is known about veterinary medical professionals' (e.g., Veterinarians, Veterinary Technicians, Veterinary Nurses) knowledge of ASA and how they may contribute to the prevention of ASA. Thus, the objective of this paper is to comprehensively study ASA in a sample of veterinary medical professionals. Eighty-eight professionals were recruited through professional associations and posts on social media to take part in a survey examining non-sexual animal abuse, ASA, and criminal justice perceptions. Results indicated that, levels of knowledge and training were much lower for ASA than non-sexual abuse. Professionals also responded punitively toward individuals who have committed sexual abuse against animals and supported long prison sentences and registries for offenders. Veterinary medical professionals were supportive of mandatory reporting of all types of abuse but did not feel prepared to testify in these cases should they go to court. These results have implications for practice as they indicate that veterinary medical professionals are not receiving enough training on abuse - particularly ASA - which could put their patients at risk of continued harm.
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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 | 0.007 |
| 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) | 0.003 | 0.001 |
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".