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Record W3158094974 · doi:10.5206/eei.v31i1.13923

Service Animals and Pet Therapy in Schools: Synthesizing a Review of the Literature

2021· review· en· W3158094974 on OpenAlexaffvenue
Virginie Abat-Roy

Bibliographic record

VenueExceptionality Education International · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAnimal welfarePsychologyInclusion (mineral)Context (archaeology)Social emotional learningMedical educationIntervention (counseling)Animal-assisted therapyCognitionApplied psychologyPedagogyPet therapyDevelopmental psychologyMedicineSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

As the academic and social-emotional needs of students in schools continue to increase, so too does the presence of dogs in educational spaces. This article aims to present an overview of past and present animal-assisted intervention practices in school settings. This comprehensive literature review examines the current state of research within this field of study. Data from 29 publications were selected according to strict inclusion and exclusion criteria. The results highlight three categories in which the presence of dogs in schools have an impact: social-emotional, cognitive, and physiological. Challenges to program implementation include health risks, cultural context, and negative effects on the animal. Due to the lack of school-based research, more study is needed, especially in order to understand the effect of dogs on the social-emotional learning of students. Finally, the welfare and training of the animals involved should be taken into consideration, and regulations regarding handler and animal training should be enforced.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.008
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.049
GPT teacher head0.441
Teacher spread0.392 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations19
Published2021
Admission routes2
Has abstractyes

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