Emotion Regulation Frequency and Self-Efficacy: Differential Associations with Affective Symptoms.


Journal

Behavior therapy
ISSN: 1878-1888
Titre abrégé: Behav Ther
Pays: England
ID NLM: 1251640

Informations de publication

Date de publication:
Sep 2024
Historique:
received: 10 10 2023
revised: 27 02 2024
accepted: 28 02 2024
medline: 23 8 2024
pubmed: 23 8 2024
entrez: 22 8 2024
Statut: ppublish

Résumé

Successful emotion regulation is a critical component of mental health, and difficulties with emotion regulation have been associated with a wide range of disorders, including anxiety and depressive disorders. However, although much is known about commonly used forms of emotion regulation such as cognitive reappraisal and expressive suppression, the relative contribution of two important facets of emotion regulation-frequency and self-efficacy-is not yet clearly established. To address this issue, we conducted two studies. Study 1 employed a community sample (cross-sectional N = 431; 4- to 5-month longitudinal N = 182). Study 2 employed a clinical sample (cross-sectional N = 132). Both assessed emotion regulation frequency and self-efficacy, as well as affective outcomes (anxiety and depressive symptoms). Findings indicated cognitive reappraisal self-efficacy appears to be a relevant variable understanding negative affect outcomes, cross-sectionally, longitudinally and in the clinical sample. Our findings support the process model of emotion regulation in affective symptomatology. Implications for theory and treatment are discussed.

Identifiants

pubmed: 39174261
pii: S0005-7894(24)00033-9
doi: 10.1016/j.beth.2024.02.009
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1004-1014

Informations de copyright

Copyright © 2024. Published by Elsevier Ltd.

Auteurs

Juan Ramos-Cejudo (J)

Camilo Jose Cela University. Electronic address: jramos@ucjc.edu.

José M Salguero (JM)

University of Malaga.

Esperanza García-Sancho (E)

University of Cordoba.

James J Gross (JJ)

Stanford University.

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Classifications MeSH