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Record W1975037549 · doi:10.1177/108705470300600302

The attributions of children with Attention-Deficit/Hyperactivity Disorder for their problem behaviors

2003· article· en· W1975037549 on OpenAlexaff
Iris Kaidar, Judith Wiener, Rosemary Tannock

Bibliographic record

VenueJournal of Attention Disorders · 2003
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPsychologyAttributionDevelopmental psychologyAttention deficit hyperactivity disorderLocus of controlCausality (physics)Clinical psychologyAttention deficitSocial psychology

Abstract

fetched live from OpenAlex

This study investigated the attributions children with ADHD make about their most problematic symptoms. Children were interviewed to determine the degree to which they felt their behavior was controllable, stable, global, and stigmatizing; and about the locus of the cause of their behavior. Participants were 16 children with ADHD (10 boys, 6 girls), and 16 children without ADHD (9 boys, 7 girls), ages 7 to 13. The present study demonstrated that children with ADHD viewed their most problematic behaviors as less within their control and more global across situations than children without ADHD. Children with ADHD were more likely than children without ADHD to view their most problematic behavior as always having been present, but were no more likely to view their most problematic behavior as persisting into the future. No significant group differences emerged on the locus of causality dimension. With regards to stigmatization, girls without ADHD reported that their behaviors can bother their teachers, parents, and peers, whereas girls and boys with ADHD did not perceive their behavior as bothersome.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.290
Teacher spread0.273 · 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 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

Citations36
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Attention DisordersSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207