Research on aggression usually aims at gaining a better understanding of its more negative aspects, such as the role and effects of aversive social interactions, hostile cognitions, or negative affect. However, there are conditions under which an act of aggression can elicit a positive affective response, even among the most nonviolent of individuals. One might experience the "sweetness of revenge" on reacting aggressively to a betrayal or social rejection. A soldier may feel elated after "shooting to kill" in the name of the flag. There are many factors that contribute to the appeal of aggression, but despite growing interest in researching these phenomena, there is still no unitary framework that organizes existing theories and empirical findings and can be applied to a model to generate testable hypotheses. This article presents a narrative review of the literature on positive-affect-related forms of aggression and explores the role of aggression in eliciting positive affect across diverse social situations and relational contexts. An integrative model that unifies existing theories and findings is proposed, with the objective to inspire and inform future research.
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http://dx.doi.org/10.1177/17456916231200421 | DOI Listing |
Front Psychol
January 2025
Department of Psychology, Rey Juan Carlos University, Alcorcón, Spain.
Introduction: Suffering from chronic pain (CP) and coping with parenthood can be challenging for parental mental health. Pain can hinder the ability to deal with demands related to parenthood, which can negatively affect their psychological well-being because of unmet caregiving expectations.
Methods: Considering the limited amount of research regarding the mental health of parents with CP, the study's main aim was to test a predictive model based on previous scientific literature, using structural equation analysis, in which parental competence and parental guilt partially mediate the relationship between parental stress and depression.
Br J Biomed Sci
January 2025
Department of Biosciences and Chemistry, Sheffield Hallam University, Sheffield, United Kingdom.
Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI's integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use.
View Article and Find Full Text PDFSyst Rev
January 2025
School of Healthcare, University of Leeds, Worsley Building, Leeds, LS2 9JT, UK.
Background: Parents and carers are increasingly expected to administer prescribed medicines to their children at home. However, parents and carers are not always able to administer medicines as directed by the prescriber and ultimately must rely on their own judgment to administer medicines safely. This process is often unseen but may contain important learning for professionals, academics, and wider society.
View Article and Find Full Text PDFSci Rep
January 2025
Fujian Engineering Technology Research Center for Tunnel and Underground Space, Huaqiao University, Xiamen, 361021, China.
The performance assessment of slopes during various disturbances is important to ensure the safe service of slopes. Due to the four characteristics of slope engineering, it is difficult for existing resilience evaluation models to accurately reflect slope performance. To address this problem, a resilience evaluation model applicable to slopes was developed in this study.
View Article and Find Full Text PDFISA Trans
January 2025
State Key Laboratory of Computer-Aided Design and Computer Graphics, Zhejiang University, Hangzhou, 310027, China; Key Laboratory of Intelligent Rescue Equipment for Collapse Accidents, Ministry of Emergency Management, Hangzhou, 310030, China; Zhejiang Laboratory, Hangzhou, 311121, China. Electronic address:
Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement.
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