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Record W3201821978 · doi:10.25035/ijpp.02.01.06

Animal-Assisted Play: A Strategy for Promoting Children’s Physical Activity Play

2021· article· en· W3201821978 on OpenAlexaff
Jennifer A. Boisvert, W. Andrew Harrell

Bibliographic record

VenueInternational Journal of Playwork Practice · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysical activityAgency (philosophy)PsychologyQuality (philosophy)Quality of life (healthcare)Animal-assisted therapyValue (mathematics)Mental healthAnimal welfareMedicinePet therapyPsychotherapistSociologyComputer sciencePhysical therapy

Abstract

fetched live from OpenAlex

This article explores how animal-assisted play might be used as a strategy in playwork to support children’s right to play, enrich their play experiences, encourage their physical activity play and better their health and life quality. Children’s interactions with pets or companion animals in free play or animal-assisted activities (AAA) can yield therapeutic benefits by increasing physical and mental health and well-being and also offer possibilities for more outdoor play, agency, risk-taking, fun and enjoyment, as well as high-quality play experiences. We invite playwork practitioners to consider the important role of animals in children’s lives and the potential value of animal-assisted play in creating opportunities for enriching children’s play and life quality.

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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.405
Teacher spread0.374 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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