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Record W2105472007 · doi:10.3138/jvme.35.4.540

The Contribution of Animals to Human Well-Being: A Veterinary Family Practice Perspective

2008· article· en· W2105472007 on OpenAlexvenueno aff
Richard Timmins

Bibliographic record

VenueJournal of Veterinary Medical Education · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Companion animalMental healthAnimal welfareVeterinary medicineMedicinePsychologyNursingMedical educationPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.445
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2008
Admission routes1
Has abstractyes

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