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Record W2129473828 · doi:10.1002/aur.1491

Self‐perception of competencies in adolescents with autism spectrum disorders

2015· article· en· W2129473828 on OpenAlexafffund
Rosaria Furlano, Elizabeth Kelley, Layla Hall, Daryl E. Wilson

Bibliographic record

VenueAutism Research · 2015
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsQueen's University
FundersQueen's UniversityChildren Neurodevelopmental Disorders Network
KeywordsPsychologyPerceptionAutismCompetence (human resources)Task (project management)Developmental psychologyAutism spectrum disorderTypically developingCognitionCognitive psychologyClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Research has demonstrated that, despite difficulties in multiple domains, children with autism spectrum disorders (ASD) show a lack of awareness of these difficulties. A misunderstanding of poor competencies may make it difficult for individuals to adjust their behaviour in accordance with feedback and may lead to greater impairments over time. This study examined self-perceptions of adolescents with ASD (n = 19) and typically developing (TD) mental-age-matched controls (n = 22) using actual performance on objective academic tasks as the basis for ratings. Before completing the tasks, participants were asked how well they thought they would do (pre-task prediction). After completing each task, they were asked how well they thought they did (immediate post-performance) and how well they would do in the future (hypothetical future post-performance). Adolescents with ASD had more positively biased self-perceptions of competence than TD controls. The ASD group tended to overestimate their performance on all ratings of self-perceptions (pre-task prediction, immediate, and hypothetical future post-performance). In contrast, while the TD group was quite accurate at estimating their performance immediately before and after performing the task, they showed some tendency to overestimate their future performance. Future investigation is needed to systematically examine possible mechanisms that may be contributing to these biased self-perceptions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.070
GPT teacher head0.345
Teacher spread0.276 · 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

Citations24
Published2015
Admission routes2
Has abstractyes

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