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Record W2995372043 · doi:10.35988/sm-hs.2019.106

CANINE-ASSISTED THERAPY AND THE IMPROVEMENT OF PHYSICAL CHARACTERISTICS IN DISABLED CHILDREN: A PILOT STUDY

2019· article· en· W2995372043 on OpenAlexaboutno aff
Ugnė Nedzinskaitė, Julija Mažeikaitė, Mindaugas Paleckaitis, Rolandas Stankevičius

Bibliographic record

VenueHealth Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMotor skillPsychologyPsychomotor learningPhysical therapyPhysical medicine and rehabilitationTest (biology)Movement assessmentTorsoAnxietyMedicineDevelopmental psychologyCognition

Abstract

fetched live from OpenAlex

Materials and methods. Three groups of mentally disabled children of different age participated in dog-assisted therapy sessions twice per week for two months. Motor skills evaluation was based on the Bruininks-Oseretsky motor skills evaluation test (short version). Isometric torso muscle endurance tests were based on Ito, McIntosh and McGill. The ability to focus and memorise exercises and the ability to understand and perform them was also evaluated. Results. Movement perception and performance, as well as ability to focus and memorise the movement sequel improved after canine-assisted exercise sessions. The most significant changes in performance were observed in the torso muscle static endurance test, push-ups, fine motor skills, and coordination (p<0.001). Conclusion. Dogs can be successfully used as motivation for the performance of various task or to lower psychological tension and anxiety during exercise sessions. It is hoped that the results of this study will be used for the development of formal dog-assisted therapy guidelines for use in physical therapy with mentally disabled children.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.154
Threshold uncertainty score0.162

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.375
Teacher spread0.339 · 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 teacher head, 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
Published2019
Admission routes1
Has abstractyes

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