Animal Welfare and the Human–Animal Bond: Considerations for Veterinary Faculty, Students, and Practitioners
Bibliographic record
Abstract
Consideration of the human-animal bond typically focuses on the benefits of companion animals to human health and well-being, but it is essential that in realizing these benefits the welfare needs of the animals, both physical and mental, are also met. Positive emotional relationships with animals are likely to increase recognition of animal sentience and so help create positive attitudes toward animals at the societal level, but, at the individual level, the animals to which humans are bonded should also benefit from the human-animal relationship. A strong human-animal bond may benefit animal welfare (e.g., by motivating an owner to commit time and funds to necessary veterinary medical treatment), but may also be the source of compromised welfare. Highly bonded owners may, for example, be reluctant to permit euthanasia on humane grounds, and the anthropomorphic nature of many human-companion animal bonds can contribute to the development of problem behaviors and obesity. The challenge for the veterinary profession is to ensure that widespread positive sentiment toward animals, which the human-animal bond generates, is translated in to human behavior and actions that are conducive to good animal welfare. This, it is suggested, can be achieved through adequate veterinary education in veterinary and animal welfare science, ethics, and communication.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".