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Record W3181271279 · doi:10.1080/00221325.2021.1945998

Longitudinal Relations between Rejection Sensitivity and Adjustment in Chinese Children: Moderating Effect of Emotion Regulation

2021· article· en· W3181271279 on OpenAlexaff
Xuechen Ding, Laura L. Ooi, Robert J. Coplan, Wen Zhang, Wenyu Yao

Bibliographic record

VenueThe Journal of Genetic Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyDevelopmental psychologyLongitudinal studySocial rejectionSensitivity (control systems)ModerationChinaSocial psychologySocial relationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The goal of the present study was to examine the moderating effect of emotion regulation in the longitudinal relations between rejection sensitivity and indices of adjustment among Chinese children. Participants were N = 590 children (Mage= 11.25 years, SD = 1.33) attending public elementary and middle schools in Shanghai, P.R. China. Measures of anxious rejection sensitivity and socio-emotional functioning were collected via self-reports and peer nominations. Among the results, rejection sensitivity significantly predicted higher levels of later internalizing problems. Moreover, emotion regulation significantly moderated (i.e. buffering effect) the relations between rejection sensitivity and later peer and emotional difficulties. The current findings suggest that rejection sensitivity poses developmental risk over time, but emotion regulation may serve as a protective factor for Chinese youth. Results are discussed in terms of the implications of rejection sensitivity and emotion regulation in Chinese culture.

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)0.001
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.303
Teacher spread0.288 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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