MétaCan
Menu
Back to cohort
Record W4223584034 · doi:10.1177/02654075221088521

Emotion suppression on relationship and life satisfaction: Taking culture and emotional valence into account

2022· article· en· W4223584034 on OpenAlexaff
Da Eun Han, Haeyoung Gideon Park, Un Ji An, So Eun Kim, Young-Hoon Kim

Bibliographic record

VenueJournal of Social and Personal Relationships · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsValence (chemistry)PsychologyIntrapersonal communicationInterpersonal communicationSocial psychologyExpressive SuppressionEmotional valenceLife satisfactionSubjective well-beingInterpersonal relationshipNegative emotionCognitive reappraisalDevelopmental psychologyCognitionHappiness

Abstract

fetched live from OpenAlex

Despite a general consensus on the negative consequences of emotion suppression in Western cultures, cross-cultural explorations to date have yielded many inconsistencies on whether such phenomena can be generalized to Eastern cultures. A set of two studies were conducted to examine the role of emotional valence in resolving such inconsistencies on both relationship satisfaction and subjective well-being. In accordance with our hypotheses, our results consistently revealed that the habitual suppression of emotions was associated with lower relationship satisfaction and subjective well-being, regardless of valence, for American participants. However, the effects of emotion suppression significantly varied by valence for Korean participants, such that suppressing negative emotions was less detrimental than suppressing positive emotions. Overall, the present study highlights the importance of considering the nature of different emotions and cultural contexts when examining the adaptiveness of emotion regulation strategies on individuals’ interpersonal and intrapersonal well-being.

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 categoriesScience and technology studies, Insufficient 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 score0.095
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
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.152
GPT teacher head0.370
Teacher spread0.217 · 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

Citations27
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Social and Personal RelationshipsSame topicCultural Differences and ValuesFrench-language works237,207