We use the gender relations perspective from feminist theorizing to investigate how gender and daily emotion work predict daily relationship quality in 74 couples (148 individuals in dating, cohabiting, or married relationships) primarily from the southwest U.S. Emotion work is characterized by activities that enhance others' emotional well-being. We examined emotion work two ways: trait (individuals' average levels) and state (individuals' daily fluctuations). We examined actor and partner effects of emotion work and tested for gender differences. As outcome variables, we included six types of daily relationship quality: love, commitment, satisfaction, closeness, ambivalence, and conflict. This approach allowed us to predict three aspects of relationship quality: average levels, daily fluctuations, and volatility (overall daily variability across a week). Three patterns emerged. First, emotion work predicted relationship quality in this diverse set of couples. Second, gender differences were minimal for fixed effects: Trait and state emotion work predicted higher average scores on, and positive daily increases in, individuals' own positive relationship quality and lower average ambivalence. Third, gender differences were more robust for volatility: For partner effects, having a partner who reported higher average emotion work predicted volatility in love, satisfaction, and closeness for women versus volatility in love and commitment for men. Neither gender nor emotion work predicted average levels, daily fluctuations, or volatility in conflict. We discuss implications and future directions pertaining to the unique role of gender in understanding the associations between daily emotion work and volatility in daily relationship quality for relational partners.
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http://dx.doi.org/10.1007/s11199-015-0495-8 | DOI Listing |
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January 2025
Department of Health Sciences, Faculty of Medicine, Lund University, Lund, Sweden.
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View Article and Find Full Text PDFBMC Public Health
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Department of Social Work, Hong Kong Baptist University, Hong Kong, China.
Background: The COVID-19 pandemic has had profound psychophysiological and socioeconomic effects worldwide. COVID-19 anxiety syndrome (CAS) is a specific cluster of maladaptive coping strategies, including perseveration and avoidance behaviours, in response to the perceived threat and fear of COVID-19. CAS is distinct from general COVID-19 anxiety.
View Article and Find Full Text PDFMedicine (Baltimore)
November 2024
Department of Sociology, Social Work and Public Health, Faculty of Labour Sciences, University of Huelva, Huelva, Spain.
This study aimed to examine the employment status of patients who have experienced ischemic heart disease one year after undergoing cardiac rehabilitation. For this, a quasi-experimental pre-post study without a control group of active workers aged 18 to 65 years diagnosed with ischemic heart disease and included in a cardiac rehabilitation programme was conducted. Sociodemographic and occupational data, cardiovascular risk factors and clinical-therapeutic data on heart disease were collected.
View Article and Find Full Text PDFActa Psychol (Amst)
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Faculty of Psychology, University of Vienna, Vienna, Austria; Vienna Cognitive Science Hub, Vienna, Austria.
Colour plays an important role in the sighted world, not only by guiding and warning, but also by helping to make decisions, form opinions, and influence emotional landscape. While not everyone has direct access to this information, even people without colour vision (i.e.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region. Electronic address:
In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification over raw EEG data. In particular, the FG-HANet extracts features not only from original inputs but also from their mirrored versions, and applies Finite Impulse Response (FIR) filters at a granularity as fine as 2-Hz to acquire fine-grained spectral information. Furthermore, to guarantee sufficient attention to hemispheric asymmetry features, we tailor a three-stage training pipeline for the FG-HANet to further boost its performance.
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