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Record W2604741675 · doi:10.5539/gjhs.v9n7p67

An Observational Study on Canine-assisted Play Therapy for Children with Autism: Move towards the Phrase of Manualization and Protocol Development

2017· article· en· W2604741675 on OpenAlexvenueno aff
Suk Chun Fung

Bibliographic record

VenueGlobal Journal of Health Science · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
FundersEducation University of Hong KongChinese University of Hong KongUniversity of Hong Kong
KeywordsObservational studyPsychosocialAutism spectrum disorderAutismPsychologyIntervention (counseling)Protocol (science)Developmental psychologyCoding (social sciences)Clinical psychologyPsychotherapistMedicinePsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Canine-assisted play therapy (CAPT) is an emerging psychosocial intervention for children with autism spectrum disorder (ASD). The present case studies used quantitative observation of experimenter-designated behavioral outcomes to examine the effectiveness of a planned CAPT intervention. By utilizing a coding system with typical and ASD-specific behavioral categories, the verbal and non-verbal social behaviors of two elementary-aged children with ASD as well as intellectual and language impairments at pre-treatment, during CAPT treatment, at post-treatment and at follow-up are systematically coded and analyzed. This study took the first steps toward making CAPT treatment manuals and research protocol available to a wider audience. Their publication will assist in standardizing the technique for replication in research and in practice.

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)0.114
metaresearch head score (Gemma)0.128
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 score0.114
Threshold uncertainty score0.602

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1140.128
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0010.002
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.124
GPT teacher head0.475
Teacher spread0.351 · 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
Published2017
Admission routes1
Has abstractyes

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