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Record W2998104435 · doi:10.5539/gjhs.v11n14p81

Family Roles in Developing Resilience Skills in School Children with Autism Spectrum Disorders

2019· article· en· W2998104435 on OpenAlexvenueno aff
Charity N. Onyishi, Maximus Monaheng Sefotho

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsTypically developingAutismPsychological resilienceAutism spectrum disorderResilience (materials science)Family resilienceDevelopmental psychologyPsychologyDeveloping countrySocial psychologyEconomic growth

Abstract

fetched live from OpenAlex

Children with Autism Spectrum (ASD) Disorders are highly vulnerable and constitute significant burdens on the families, schools and the global economy. Developing resilience in children with autism spectrum disorders is a major challenge to both teachers and parents. This article discusses the roles of the family in building resilience among children with ASD. Firstly, the article explored the roles of parents in developing resilience in children with ASD. Secondly, the roles of siblings in developing resilience in children with ASD were examined. Thirdly, the roles of family socio-economic/cultural contexts in developing resilience in children with ASD were investigated. Finally, we discussed researches on best practices for resilience in children with ASD and directions for educational practices and future research.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.358
Teacher spread0.344 · 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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

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