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Record W3039570733 · doi:10.1177/1362361320933048

Temperament influences the relationship between symptom severity and adaptive functioning in children with autism spectrum disorder

2020· article· en· W3039570733 on OpenAlexafffund
Vivian Lee, Eric Duku, Lonnie Zwaigenbaum, Teresa Bennett, Péter Szatmári, Mayada Elsabbagh, Connor M. Kerns, Pat Mirenda, Isabel M. Smith, Wendy J. Ungar, Tracy Vaillancourt, Joanne Volden, Charlotte Waddell, Anat Zaidman‐Zait, Ann Thompson, Stelios Georgiades

Bibliographic record

VenueAutism · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsSimon Fraser UniversityUniversity of OttawaUniversity of British ColumbiaMcGill UniversityDalhousie UniversityUniversity of TorontoSickKids FoundationUniversity of AlbertaMcMaster University
FundersKids Brain Health NetworkCanadian Institutes of Health ResearchAlberta Innovates - Health SolutionsSinneave Family FoundationAutism Speaks
KeywordsTemperamentAutism spectrum disorderAutismPsychologyAdaptive functioningTraitDevelopmental psychologyAdaptive behaviorClinical psychologyHigh-functioning autismPersonality

Abstract

fetched live from OpenAlex

Temperament is a construct that is relatively stable over time but varies between individuals. Research suggests that children with autism spectrum disorder have a ‘reactive’ temperament profile when compared to peers with or without disabilities. However, our understanding of how temperament varies within children with autism and how it relates to child symptoms and outcomes is limited. This study aimed to (a) explore the variation of individual temperament traits within a sample of school-aged children with autism to determine whether subgroups of children with similar trait profiles emerge and (b) examine whether temperament influences the relationship between autism symptoms and adaptive functioning outcomes. Results revealed that children with autism can be classified empirically into two distinct profiles – ‘Even’ and ‘Reactive’ temperaments. Correlational and hierarchical regression analyses indicated that both temperament profiles and baseline symptom severity predicted adaptive functioning outcomes 1 year later. There was a significant interaction between temperament and symptom severity, suggesting temperament can influence the impact of increasing symptom severity on adaptive functioning skills in children with autism. Study findings highlight the importance of considering temperament in understanding the individual differences that influence the development of daily functioning and developmental outcomes in children with autism. Lay Abstract Temperament is often thought of as behavioural traits that are relatively stable over time but can vary between individuals. Children diagnosed with autism spectrum disorder are often characterized as having ‘reactive’ and ‘negative’ temperaments when compared to same-aged peers with or without disabilities, which can negatively impact the development of adaptive functioning skills but little is known about variations of temperament between individual children diagnosed with autism spectrum disorder. This study aimed to (a) explore the variation of individual temperament traits within a sample of school-aged children with autism spectrum disorder to determine whether subgroups with similar trait profiles emerge and (b) examine whether temperament influences the relationship between autism symptoms and adaptive functioning outcomes. Results from our dataset suggest that children diagnosed with autism spectrum disorder fit under two profiles: ‘even’ and ‘reactive’. Furthermore, our analysis shows that temperament can influence the impact of increasing symptom severity on adaptive functioning skills in children with autism spectrum disorder. Study findings highlight the importance of considering temperament when trying to understand the individual differences that influence the development of functioning and developmental outcomes in children with autism spectrum disorder.

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 score0.068
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.277
Teacher spread0.234 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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