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Record W7117579113 · doi:10.1177/19485506251407414

Rumination in Daily Life Is Linked to Poorer Psychological Health in the United States Than in Japan

2025· article· en· W7117579113 on OpenAlexfundno aff
Danfei Hu, Yuri Miyamoto, Yulia Chentsova-Dutton, Lisya Kaspi, Renee J. Thompson, Tomotaka Okuyama, Maya Tamir

Bibliographic record

VenueSocial Psychological and Personality Science · 2025
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceAzrieli FoundationIsrael Science Foundation
KeywordsRuminationTraitPsychological healthPsychological well-beingMental healthDepressive symptomsAcculturationAssociation (psychology)

Abstract

fetched live from OpenAlex

Rumination has been linked to worse psychological health. Yet, some cross-cultural research suggests that associations between trait rumination and psychological health may be stronger in Western than East Asian cultures. This investigation tested, for the first time, whether using rumination to regulate emotions in daily life is differentially linked to psychological health in different cultural contexts. In two preregistered studies that involved baseline (trait level) and ecological momentary assessments (state level), we tested links between rumination and psychological health in college student samples from the United States ( N s = 128 and 105) and Japan ( N s = 93 and 113), at both trait and state levels. In both studies, rumination in daily life was associated with lower well-being, but these associations were stronger in the United States than in Japan. These findings emphasize the importance of culturally sensitive approaches to understanding emotion regulation and psychological health.

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)0.000
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.104
GPT teacher head0.460
Teacher spread0.355 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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