Animal Abuse Reporting and the Ethical Role of Veterinarians: A Comparative Review of Practices in South Korea, Canada, and the United States
Bibliographic record
Abstract
Animal abuse reporting represents a crucial ethical responsibility for veterinarians globally, with cultural, legal, and societal factors significantly impacting practices. This comparative narrative review investigates veterinary reporting practices across South Korea, Canada, and the United States. A narrative review was conducted using systematic searches across PubMed, Google Scholar, Web of Science, and Scopus databases, employing jurisdiction-specific terms. Additional searches encompassed state-specific legal databases, professional licensing board records, and disciplinary action reports. To investigate reporting systems, enforcement mechanisms, and outcomes, legal documents, policy analyses, and empirical studies from 2010 to 2025 were analyzed. Findings revealed stark contrasts between voluntary and mandatory systems. South Korea's voluntary framework caused significant underreporting. Canada's provincial mandatory frameworks exhibited significantly higher levels of compliance, as a result of the strength of their statutory enforcement mechanisms. The United States presented a mixed landscape, wherein approximately 24 states mandated reporting with license revocation penalties, whereas others maintained voluntary systems. Cultural factors, professional autonomy concerns, and enforcement mechanisms shaped reporting behaviors and animal welfare outcomes. South Korea requires legislative changes implementing graduated mandatory reporting systems with legal protections and cultural adaptation strategies. Benchmarking against Canadian and American models demonstrates that mandatory reporting with appropriate enforcement mechanisms shows potential to enhance animal welfare protection through increased reporting rates and systematic intervention pathways, though direct causal evidence linking reporting to measurable welfare outcomes remains limited.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".