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Record W2925154076 · doi:10.1163/15685306-12341537

On-Campus Animal-Assisted Therapy Events

2018· article· en· W2925154076 on OpenAlexaff
Alisa D. McArthur, Corinne Syrnyk

Bibliographic record

VenueSociety and Animals · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsSt. Mary's University
Fundersnot available
KeywordsMoodPsychologyLiberal arts educationEvent (particle physics)Clinical psychologyInstitutionThe artsMedical educationMedicineSociologyVisual artsPolitical scienceHigher educationSocial scienceArt

Abstract

fetched live from OpenAlex

Abstract Post-secondary students are experiencing more stress than ever before. In an attempt to help alleviate some of this stress, animal-assisted therapy (AAT) events were held on the campus of a small liberal arts institution just prior to final exams in the Fall and Winter terms. All students were invited to mingle with dogs and handlers from a local AAT advocacy group. In Study 1, students were surveyed following the events held in the Fall and Winter and self-reported an improved mood as a result of the events as well as being extremely satisfied with the experience. Similarly, Study 2, held in the subsequent Fall, replicated the findings from Study 1. In addition, the Brief Mood Inspection Scale, administered before and after the event, found students’ mood improved on all three subscales. The implications for future research to fully assess the impact of such events on students 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0520.008

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 head0.027
GPT teacher head0.351
Teacher spread0.324 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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