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Record W3109483708 · doi:10.36941/mjss-2020-0061

Adaptive Behavior in Children with Intellectual Disabilities

2020· article· en· W3109483708 on OpenAlexaboutno aff
Hazir Elshani, Eglantina Dervishi, Silva Ibrahımı, Altin Nika, Mimoza Maloku Kuqi

Bibliographic record

VenueMediterranean Journal of Social Sciences · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsVineland Adaptive Behavior ScaleIntellectual disabilityAdaptive functioningAdaptive behaviorPsychologyBorderline intellectual functioningAdaptive behaviourDevelopmental psychologyRaven's Progressive MatricesCognitionIntelligence quotientActivities of daily livingClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Children with intellectual disabilities experience deficits in all the areas of adaptive function and some other aspects, unfortunately little is known about the independent functioning among gender and age related to these impairments in this type of neurogenetic disorders as intellectual disabilities. Adaptive behavior is essential for an optimal functioning in these categories. 53 participants aged between 5 and 11 in school years have been administered the Vineland Adaptive Behavior Scales, Montreal Cognitive Assessment scale (MoCA) and Raven's Standard Progressive Matrices (IQ). Motor abilities are the area that is more affected in children with intellectual disabilities with a significant impairment appeared at an early age, which remain low even in the following years. These differences are potentially oriented by the etiologies related to the disorder. Adaptive behavior is an important area of challenge for children with intellectual disabilities throughout their development. Daily living skills and competencies are also a significant strength in relation to other areas of adaptive functioning.

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 score0.244
Threshold uncertainty score0.228

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 head0.038
GPT teacher head0.269
Teacher spread0.230 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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