The Contribution of Animals to Human Well-Being: A Veterinary Family Practice Perspective
Bibliographic record
Abstract
There is considerable evidence that humans can benefit both physically and emotionally from a relationship with companion animals, a phenomenon known as the human-animal bond (HAB). This has not only increased the demand for veterinary services to meet the needs of these non-human family members and their owners, but it has also transformed the nature of those services from reactive medicine and surgery to proactive prevention and wellness. The emotional component of the HAB requires the veterinarian to have a solid understanding of the nature of the attachment between client and pet, and an ability to educate the client about proper care of the animal in order to optimize the relationship. Paying attention to the relationship between client and patient also positions the veterinary family practitioner to refer the client to appropriate community resources for physical, emotional, or other needs of the client that may become apparent during the veterinarian-client interaction. By achieving physical and mental health objectives for patients and collaborating with human health care services, the veterinary family practitioner contributes to the well-being of both patient and client. This new face of veterinary family practice requires research and education in fields that have not traditionally been a part of veterinary training.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".