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Record W2912389049 · doi:10.1037/fam0000501

The development of internalizing problems in early childhood: The importance of sibling clustering.

2019· article· en· W2912389049 on OpenAlexafffund
Ella Daniel, Michelle Rodrigues, Jennifer M. Jenkins

Bibliographic record

VenueJournal of Family Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsSiblingPsychologySibling relationshipDevelopmental psychologySocializationCluster analysisContext (archaeology)PsycINFOStatistics

Abstract

fetched live from OpenAlex

Within family sibling clustering of internalizing problems is examined during the early childhood period. Sibling clustering, the ongoing sibling similarity in internalizing problems, may be a result of heritability of internalizing problems, as well as shared environmental effects. Clustering may also result from the time-varying influence of sibling socialization, where 1 sibling is teaching or modeling internalizing problems to the other sibling. We compared a traditional cross-lagged panel model with a recently developed multilevel statistical model that differentiates the 2 mechanisms. Sibling clustering was operationalized as the family level, time-invariant variance in internalizing problems, and differentiated from sibling socialization, the cross-lagged (time-varying) association between earlier child behavior and later sibling behavior. A 3-wave longitudinal study tracked 916 children (age M = 3.46, SD = 2.23) in 397 families using a 2-parent composite score of internalizing problems. Results suggest the importance of accounting for sibling clustering in the context of a panel study, as its inclusion in the model eliminated the identified time-varying, sibling socialization effects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.001
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 score0.065
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.039
GPT teacher head0.320
Teacher spread0.282 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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