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Record W4386152762 · doi:10.1080/09297049.2023.2247603

Environmental predictors of children’s executive functioning development

2023· article· en· W4386152762 on OpenAlexaff
James D. Lynch, Yingying Xu, Kimberly Yolton, Jane Khoury, Aimin Chen, Bruce P. Lanphear, Kim M. Cecil, Joseph M. Braun, Jeffery N. Epstein

Bibliographic record

VenueChild Neuropsychology · 2023
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsSimon Fraser University
FundersNational Institute of Environmental Health Sciences
KeywordsPsychosocialIntervention (counseling)PsychologyCohortMedicineClinical psychologyGerontologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Executive functioning (EF) abilities develop through childhood, but this development can be impacted by various psychosocial environmental influences. Using longitudinal data from the Health Outcome and Measures of the Environment (HOME) Study, a prospective pregnancy and birth cohort study, we examined if psychosocial environmental factors were significant predictors of EF development. Study participants comprised 271 children and their primary caregivers (98.5% mothers) followed from birth to age 12. We identified four distinct EF developmental trajectory groups comprising a consistently impaired group (13.3%), a descending impairment group (27.7%), an ascending impairment group (9.95%), and a consistently not impaired group (49.1%). Higher levels of maternal ADHD and relational frustration appear to be risk factors for increased EF difficulty over time, while higher family income may serve as a protective factor delaying predisposed EF impairment. Important intervention targets might include teaching positive and effective parenting strategies to mothers whose children are at risk for EF dysfunction.

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 score0.015
Threshold uncertainty score0.667

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)0.0000.001

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 head0.021
GPT teacher head0.275
Teacher spread0.254 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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