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Record W3094562992 · doi:10.1177/0033294120968086

Interpersonal Emotion Regulation and Psychological Distress: What Is the function of Negative Mood Regulation Expectancies in This Relationship?

2020· article· en· W3094562992 on OpenAlexaff
Elçin Ray-Yol, Ayşe Altan‐Atalay

Bibliographic record

VenuePsychological Reports · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsychologyPsychological distressMoodDistressAssociation (psychology)Interpersonal communicationCoping (psychology)Clinical psychologyInterpersonal relationshipDevelopmental psychologyAnxietySocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Individuals’ tendency to use their interactions with others in the management of their own emotions is called Interpersonal Emotion Regulation (IER). Limited studies have explored the association between IER and psychological distress with none focusing on the role of mediating variables in this relationship. The current study aims to explore the role of negative mood regulation expectancies (NMRE), which is defined as one’s confidence in the effectiveness of their coping skills while dealing with difficult emotions, as a possible mechanism underlying the association between IER and psychological distress. The data were collected from 204 (164 women) Turkish speaking individuals whose age ranges between 18 and 32 ( M = 22.78, SD = 3.21). The participants completed measures of IER, NMRE and psychological distress. The results have indicated that NMRE has a significant mediating role in the relationship of Soothing dimension of IER with psychological distress. The present findings highlighted the maladaptive function of Soothing as an IER strategy in addition to shedding light on the important role of NMRE in this relationship.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.056
GPT teacher head0.313
Teacher spread0.257 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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