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Walking Sole Mates: Dogs Motivating, Enabling and Supporting Guardians' Physical Activity

2013· article· en· W1966691885 on OpenAlexafffund
Joan Higgins, Viviene A. Temple, Holly Murray, Ellen Kumm, Ryan E. Rhodes

Bibliographic record

VenueAnthrozoös · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health ResearchUniversity of Victoria
KeywordsHUBzeroAnimal-assisted therapyAnimal welfarePet therapyPsychologyIntervention (counseling)HappinessPhysical activityHealth benefitsSocial psychologyMedicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Dog walking is receiving increasing attention in the public health literature as a strategy to improve dog guardians' physical activity levels. Quantitative research suggests that dog guardians walk more often and for longer than non dog-guardians, and offers suggestions as to the reasons for these differences. The purpose of this study was to qualitatively explore dog guardians' walking practices and relationships with their dogs, to better understand how and why dog walking might become an intervention point to enhance physical activity levels. Five focus groups and two interviews were conducted with 16 adult dog guardians. Following an initial analysis of the results, 10 additional dog guardians were individually interviewed to review and confirm the findings. Four themes emerged to explain the dog-walking phenomenon: Transcending the human–animal distinction; Dogs as walking sole mates; Activity/health benefits; and Dogs as social conduits. We argue that an empathetic stance benefits dog guardians because, as valued family members whose health and happiness they are responsible for, their canine companions serve to motivate, enable, and sustain walking behaviors.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.348
Teacher spread0.332 · 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

Citations42
Published2013
Admission routes2
Has abstractyes

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