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Record W4383817678 · doi:10.1177/21676968231188754

Beyond Cuddling Canines: Exploring Students’ Perceptions of the Importance of Touch in an On-Campus Canine-Assisted Intervention

2023· article· en· W4383817678 on OpenAlexaff
Freya L.L. Green, John-Tyler Binfet

Bibliographic record

VenueEmerging Adulthood · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPsychologyFeelingIntervention (counseling)Psychological interventionPopularityMoodAffect (linguistics)Animal-assisted therapyClinical psychologyPerceptionAnimal welfarePet therapySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Despite the increasing popularity of canine-assisted interventions (CAIs) across a variety of contexts, there is a paucity of literature exploring participants’ perceptions of the mechanisms that contribute to the benefits reported by researchers. This study aimed to expand on the knowledge obtained by our Phase I findings and to better understand undergraduate participants’ ( N = 280) perceptions and experiences of direct, physical contact versus indirect, close contact with therapy dogs and spending time with therapy dog handlers alone. Participants’ responses revealed that direct contact with therapy dogs was more likely to elicit benefits in positive affect, including reducing stress and improving mood, than those in the indirect or handler-only groups. Conversely, spending time with the handlers only was more likely to elicit social benefits, such as feeling more connected and less homesick. These findings hold implications for post-secondary CAIs and for therapy dog programs.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.376
Teacher spread0.338 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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