Bond-Centered Veterinary Practice: Lessons for Veterinary Faculty and Students
Bibliographic record
Abstract
We are currently experiencing a paradigm shift in attitudes to companion animals, in part due to our greater understanding of the health and social benefits associated with the human-animal bond (HAB). Recent demographic changes, including smaller family size, increased longevity, and a higher incidence of relationship breakdown, have resulted in a greater dependence on pets for companionship and social support. It is therefore important for the veterinary profession to understand the HAB, keep abreast of knowledge in this field, and apply research findings to help our clients, their companion animals, and the wider society in which we live. How can veterinarians incorporate the HAB into their practices for the benefit of people and animals, and what are the effects of using a bond-centered approach? This article addresses this question, and arises from the experience of a veterinarian who introduced a bond-centered approach to her practice in the United Kingdom over 20 years ago.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".