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Record W3153611356 · doi:10.20849/jed.v5i1.887

Effects of Touch on Students’ Stress, Happiness, and Well-Being During Animal-Assisted Activities

2021· article· en· W3153611356 on OpenAlexaffabout
Laura Sokal, Brianne Bartel, Taylor Martin

Bibliographic record

VenueJournal of Education and Development · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsHappinessMental healthPromotion (chess)PsychologyStress (linguistics)Well-beingClinical psychologySocial psychologyMedical educationApplied psychologyDevelopmental psychologyMedicinePolitical sciencePsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Post-secondary institutions across North America have adopted animal-assisted activities as a way to promote better mental health in their students. The current research study of 242 Canadian college and university students sought to contribute to our collective understanding of the aspects of the programs and characteristics of students that are related to promotion of better mental health in post-secondary students including decreased stress, and increased happiness and well-being. Results of a repeated measures design showed that students demonstrated greater positive effects on stress, happiness, and well-being when they touched dogs as compared to when they observed them. Furthermore, positive mental health outcomes were correlated with greater durations of contact as well as with higher levels of animal affiliation in students. Implications for post-secondary institutions are discussed.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.321
Teacher spread0.312 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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