MétaCan
Menu
Back to cohort
Record W1969700902 · doi:10.3109/17518423.2014.927017

Resilience and emotional intelligence in children with high-functioning autism spectrum disorder

2014· article· en· W1969700902 on OpenAlexaff
Adam W. McCrimmon, Ryan L. Matchullis, Alyssa A. Altomare

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2014
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAutism spectrum disorderPsychologyPsychological resilienceHigh-functioning autismSpectrum disorderDevelopmental psychologyAutismResilience (materials science)Emotional intelligenceIntervention (counseling)Psychological interventionPopulationClinical psychologyTypically developingMedicinePsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

PURPOSE: This article presents the results of an investigation of resilience factors and their relation to emotional intelligence (EI) as an area of potential strength for children with high-functioning autism spectrum disorder (HFASD). Based upon previous research with young adults, it was hypothesized that children with HFASD would demonstrate reduced EI and differential relations between EI and resilience as compared to typically developing (TD) children. METHODS: Forty children aged 8-12 years (20 with HFASD and 20 TD control children) completed measures of resilience and EI. RESULTS: Children with HFASD did not significantly differ from TD children on either measure. However, several significant correlations between resilience and EI were found in the HFASD sample. CONCLUSIONS: The findings suggest that EI may be a unique area of interest for this population, particularly for interventions that propose to capitalize upon potentially inherent strengths. Implications of these results for intervention are discussed.

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 score0.077
Threshold uncertainty score0.503

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 head0.007
GPT teacher head0.277
Teacher spread0.270 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental NeurorehabilitationSame topicResilience and Mental HealthFrench-language works237,207