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Record W2248515655 · doi:10.1080/09297049.2015.1065961

Parent-report and performance-based measures of executive function assess different constructs

2015· article· en· W2248515655 on OpenAlexafffund
Kayla D. Ten Eycke, Deborah Dewey

Bibliographic record

VenueChild Neuropsychology · 2015
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of CalgaryAlberta Children's Hospital
FundersCanadian Institutes of Health Research
KeywordsPsychologyNeuropsychologyReading (process)Executive functionsDevelopmental psychologySet (abstract data type)Neuropsychological assessmentCognitive psychologyCognitionNeuroscience

Abstract

fetched live from OpenAlex

A total of 405 children of 5-18 years of age were administered performance-based and parent-report measures of executive function (EF), and measures of motor, attention, reading, and mathematics performance. Attention, reading, and mathematics abilities were associated with a parent-report measure of EF. Reading and mathematics abilities were also associated with performance-based measures of EF, including the Animal Sorting, Inhibition, and Response Set subtests of the Developmental NEuroPSYchological Assessment-II. In contrast, motor functioning was only associated with performance-based measures of EF. Findings suggest that different constructs of EF are measured by parent-report versus performance-based measures, and that these different constructs of EF are associated with different neurodevelopmental processes.

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)0.004
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.114
GPT teacher head0.317
Teacher spread0.202 · 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

Citations102
Published2015
Admission routes2
Has abstractyes

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