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Record W3039843561 · doi:10.1108/aia-01-2020-0008

Social and motor skills of children and youth with autism from the perspectives of caregivers

2020· article· en· W3039843561 on OpenAlexaff
Brianne Redquest, Pamela J. Bryden, Paula C. Fletcher

Bibliographic record

VenueAdvances in Autism · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsWilfrid Laurier UniversityCentre for Addiction and Mental Health
Fundersnot available
KeywordsAutismPsychologyDevelopmental psychologyMotor skill

Abstract

fetched live from OpenAlex

Purpose This study aims to explore social and motor impairments of children with autism through the perspectives of their caregivers. Social and motor deficits among people with autism are well documented. There is support to suggest a reciprocal relationship between social and motor deficits among people with autism, in that social deficits can act as a barrier to motor skill development and motor deficits can act as a barrier to social skill development. Design/methodology/approach This study explored social and motor impairments of children with autism through the perspectives of eight caregivers of children with autism. Findings Many salient findings emerged from the interviews conducted with caregivers, particularly concerning the social and motor development of their children. The relationships between their children’s social and motor deficits were also highlighted. Research limitations/implications It is important that health-care professionals educate parents about the consequences of motor impairments or delays and their associations with the development of social skills. As such, routine motor skill monitoring and assessments by caregivers and health-care professionals should be encouraged. Originality/value To the best of authors’ knowledge, this is the first paper to investigate motor and social deficits of children with autism from the caregivers’ perspectives.

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 score0.305
Threshold uncertainty score0.325

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 studies0.0000.001
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 head0.016
GPT teacher head0.315
Teacher spread0.299 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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