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Record W4250930632 · doi:10.24124/2012/bpgub799

People with pets: Understanding the influence of human-companion animal attachment on empathy and resilient coping in adulthood.

2012· dissertation· en· W4250930632 on OpenAlexaff
Kelly L. Stickle

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsCanadian HeritageBurman UniversityLibrary and Archives Canada
Fundersnot available
KeywordsEmpathyPsychologyCoping (psychology)Prosocial behaviorDevelopmental psychologyInterpersonal communicationCompanion animalHuman animalHuman researchClinical psychologySocial psychologyPsychotherapistLivestock

Abstract

fetched live from OpenAlex

This research investigated the association of current human-companion animal attachment with adult levels of empathy and resilient coping. Various research findings have reported benefits from people interacting with companion animals. A better understanding is needed of the human-companion animal relationship, and the associations which that relationship has with human prosocial and protective factors. Pet-owning adults (n = 352) completed an online survey measuring attachment with a current pet, interpersonal empathy, resilient coping, and current attachment with another adult as a possible covariate. Current human-animal attachment does not appear to be related to current human attachment. There are not significant associations between current human-animal attachment and overall empathy or any of the measured dimensions of empathy, or with resilient coping. Institutions, therapists, and other practitioners of animal-assisted therapies may not need to rely on the formation of a strong human-companion animal bond in order for some benefits to occur. --P. ii.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.353
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2012
Admission routes1
Has abstractyes

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