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Record W1900587016 · doi:10.1111/bjdp.12105

Executive functions in kindergarteners with high levels of disruptive behaviours

2015· article· en· W1900587016 on OpenAlexafffund
Sébastien Monette, Marc Bigras, Marie‐Claude Guay

Bibliographic record

VenueBritish Journal of Developmental Psychology · 2015
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversité du Québec à MontréalCentre Jeunesse de Quebec
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyNormativeExecutive functionsDevelopmental psychologyWorking memoryComorbidityConduct disorderClinical psychologyAttention deficit hyperactivity disorderAttention deficitCognitionPsychiatry

Abstract

fetched live from OpenAlex

Executive function (EF) deficits have yet to be demonstrated convincingly in children with disruptive behaviour disorders (DBD), as only a few studies have reported these. The presence of EF weaknesses in children with DBD has often been contested on account of the high comorbidity between DBD and attention-deficit/hyperactivity disorder (ADHD) and of methodological shortcomings regarding EF measures. Against this background, the link between EF and disruptive behaviours in kindergarteners was investigated using a carefully selected battery of EF measures. Three groups of kindergarteners were compared: (1) a group combining high levels of disruptive behaviours and ADHD symptoms (COMB); (2) a group presenting high levels of disruptive/aggressive behaviours and low levels of ADHD symptoms (AGG); and (3) a normative group (NOR). Children in the COMB and AGG groups presented weaker inhibition capacities compared with normative peers. Also, only the COMB group showed weaker working memory capacities compared with the NOR group. Results support the idea that preschool children with DBD have weaker inhibition capacities and that this weakness could be common to both ADHD and DBD.

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 score0.035
Threshold uncertainty score0.526

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 head0.067
GPT teacher head0.346
Teacher spread0.279 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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