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Record W2345381057 · doi:10.5539/ijps.v8n2p111

Social Information Processing and Reactive and Proactive Aggression among Children with ADHD

2016· article· en· W2345381057 on OpenAlexvenueno aff
Muhammad Ahmed Hammad, Huda Shaaban Awed

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsAggressionPsychologySocial information processingSocializationDevelopmental psychologyInformation processingClinical psychologyCognitionPsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

<p>This study examined the social information processing qualities among children with reactive and proactive aggression among children with Attention Deficit Hyperactive Disorder (ADHD). It enrolled a total of 112 Saudi school children (62 boys, 50 girls; mean age = 9.26 years, SD = 1.98) of which 51 were diagnosed with ADHD and 61 typically developing peers. Data on children’s social informational processing and type of aggression displayed were gathered and analyzed for group differences by diagnosis and gender within diagnosis. Findings suggest gaps in social information processing and elevated aggression levels among children with ADHD compared to typical others. Male children with ADHD to present mostly with proactive aggression and self-serving information processing. Female ADHD were characterized by reactive aggression and selective information processing. Implicit socialization processes might explain the differences in social information processing and type of aggression among male and female students with Arabic culture background.</p><p><br /><strong></strong></p>

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 score0.373
Threshold uncertainty score0.165

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 communication0.0000.001
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 head0.076
GPT teacher head0.424
Teacher spread0.348 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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