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Record W4413861346 · doi:10.1002/ijop.70101

Adversity is Differentially Related to Anger and Sadness Regulation in Newcomer Refugee Children

2025· article· en· W4413861346 on OpenAlexafffundabout
Ju‐Hyun Song, Joanna Peplak, Tina Malti

Bibliographic record

VenueInternational Journal of Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthSimon Fraser University
FundersYonsei UniversityPublic Health Agency of Canada
KeywordsSadnessAngerPsychologyRefugeeDevelopmental psychologySocial psychologyClinical psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This study employed developmental niche frameworks to examine how adversity at the child- and parent-levels, as well as at the relational level through parental discipline strategies, was associated with refugee newcomer children's emotion regulation. Participants were 128 Syrian newcomer children (52% girls; ages 5-15 years) and their mothers who have been resettling in Canada. Mothers and children reported adverse life experiences in an interview, and mothers reported parental discipline strategies and their children's emotion regulation via a questionnaire. Simultaneous path analyses revealed that mothers' adverse life events predicted better sadness regulation in children, while children's own adverse life events predicted poorer anger regulation. Mothers' power assertion was negatively associated with anger regulation, while love withdrawal was associated with better sadness and anger regulation. Universality and cultural specificity of the functions of maternal discipline strategies are discussed. These findings may inform the development of practices to support newcomer children and families' social-emotional wellbeing.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.380
Teacher spread0.367 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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