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Record W1866023217 · doi:10.5539/mas.v9n11p38

Recent Technologies to Improving Social and Communication Skills in Children with ASD: Systematization of Approaches and Methods

2015· article· en· W1866023217 on OpenAlexvenueno aff
Albina A. Nesterova, Rimma M. Aysina, Т Ф Суслова

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationAutismPsychologyPsychological interventionSocial skillsIntervention (counseling)Applied behavior analysisAutism spectrum disorderTrainerApplied psychologyDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

<p>In the article recent technologies of formation and development of social and communication skills in children with autism spectrum disorders are analyzed. A systematic review summarizes the most effective and verified interventions of support to socialization of children with ASD: applied behavior analysis (ABA); cognitive-behavioral training (CBT); social stories method; social skills training (SST). We pay special attention to virtual technologies and video simulations so these methods allow to form social skills in children with ASD more efficiently and psychologically safe. Problems and prospects of using virtual technologies for children with ASD needs are discussed.<strong> </strong>The specificity of Russian practical experience and researches in development of heuristic technologies of development of social communication of persons with ASD is described: animal-assisted therapy, somatosensory correction, author art therapy and folk forms of intervention. On the basis of analytical work it is concluded that the gap between theory and practice needs to be neutralized, when scientifically unfounded practical developments are introduced in helping autistic people and researches of scientists are not always verified in an empirical manner.</p>

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.070
GPT teacher head0.339
Teacher spread0.269 · 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 designBench or experimental
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

Citations7
Published2015
Admission routes1
Has abstractyes

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