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Record W2046432509 · doi:10.1080/09297049.2013.796919

Validation of the BRIEF-P in a sample of Canadian preschool children

2013· article· en· W2046432509 on OpenAlexaffabout
Eric Duku, Tracy Vaillancourt

Bibliographic record

VenueChild Neuropsychology · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of OttawaMcMaster University
Fundersnot available
KeywordsPsychologyConstruct (python library)Developmental psychologyConstruct validityContext (archaeology)Executive functionsInternal consistencySample (material)Rating scaleWorking memoryConsistency (knowledge bases)Clinical psychologyPsychometricsCognitionPsychiatry

Abstract

fetched live from OpenAlex

The Behavior Rating Inventory for Executive Function-Preschool (BRIEF-P) is an instrument designed to assess preschoolers' executive function (EF) in the context of where the behavior occurs. The present study examined the psychometric properties and measurement structure of the BRIEF-P using parents' and teachers' reports on 625 typically developing children aged 25 to 74 months. Results indicated that the BRIEF-P scales had good internal consistency and convergent validity in this sample of children. However, the measurement models examined exhibited poor fit statistics and showed that the EF construct was not unidimensional but rather multidimensional with interrelated subconstructs. Further analyses showed that three of the clinical scales (Emotional Control, Plan/Organize, and Working Memory) were unidimensional and invariant across informant. The other two clinical scales (Inhibit and Shift) were multidimensional and differed by informant. Results support a multidimensional construct of EF and, accordingly, different measurement models are proposed by informant.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.904
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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

Citations52
Published2013
Admission routes2
Has abstractyes

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