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Record W4402893578 · doi:10.1017/s0954579424001524

Family shapes child development: The role of codevelopmental trajectories of interparental conflict and emotional warmth for children’s longitudinal development of internalizing and externalizing problems

2024· article· en· W4402893578 on OpenAlexaff
Martina Zemp, Shichen Fang, Matthew D. Johnson

Bibliographic record

VenueDevelopment and Psychopathology · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of AlbertaConcordia UniversityUniversity of Lethbridge
FundersDeutsche Forschungsgemeinschaft
KeywordsPsychologyDevelopmental psychologyLongitudinal studyLongitudinal dataLatent growth modelingFamily conflictGermanChild developmentEmotional developmentSocial changeDemography

Abstract

fetched live from OpenAlex

Abstract This study aimed (1) to identify distinct family trajectory profiles of destructive interparental conflict and parent-child emotional warmth reported by one parent, and (2) to examine whether these codevelopmental profiles were associated with the longitudinal development of children and adolescents’ self-reported internalizing and externalizing problems. Six longitudinal data waves from the German Family Panel (pairfam) study (Waves 2–7) from 722 parent-child dyads were used (age of children and adolescents in years: M = 10.03, SD = 1.90, range = 8–15; 48.3% girls; 73.3% of parents were native Germans). Data were analyzed using growth mixture and latent growth curve modeling. Two classes, harmonious and conflictual-warm families, were found based on codevelopmental trajectories of interparental conflict and emotional warmth. These family profiles were linked with the development of externalizing problems in children and adolescents but not their internalizing problems. Family dynamics are entangled in complex ways and constantly changing, which appears relevant to children’s behavior problems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.282
Teacher spread0.256 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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