Previous research has shown that that evaluative verbal information (praise and criticism) conveys different affective values: criticism is perceived as unpleasant while praise is generally considered pleasant. Here, using praise and criticism in Chinese, we investigated how affective value is modulated in men and women, depending on the particular attribute (personality vs. appearance) targeted by social comments. Results showed that whereas praise was rated as pleasant and criticism as unpleasant overall, criticizing personality reduced pleasantness more than criticizing appearance. In men, moreover, criticism of personality was deemed more unpleasant than criticism of appearance while personality-targeted praise was rated more pleasant than appearance-targeted praise. This effect was absent in women and consistent with men's higher arousal ratings for personality- relative to appearance-targeted comments. Our findings suggest that men are more concerned about external perception of their personality than that of their appearance whereas women's affective judgment is more balanced. These gender-specific results may have implications for topic selection in evaluative social communication.
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http://dx.doi.org/10.3389/fpsyg.2019.00078 | DOI Listing |
J Exp Psychol Gen
January 2025
Department of Cognitive and Psychological Sciences, Nagoya University.
Judgments of attractiveness have many important social outcomes, highlighting the need to understand how people form these judgments. One aspect of appearance that impacts perceptions of attractiveness is facial femininity/masculinity (sexual dimorphism). However, extant research has focused primarily on White, Western, heterosexual participants' preferences for femininity/masculinity in White faces, limiting generalizability.
View Article and Find Full Text PDFAm J Drug Alcohol Abuse
January 2025
Department of Psychology, University of California, Los Angeles, CA, USA.
There has been a dramatic rise in alcohol consumption and alcohol use disorder (AUD) among women. Recently, the field has made substantial progress toward better understanding sex and gender differences in AUD. This research has suggested accelerated progression to AUD and associated health consequences in women, a phenomenon referred to as "telescoping.
View Article and Find Full Text PDFAddiction
January 2025
Institute for Mental Health Policy Research, Centre for Addiction and Mental Health (CAMH), Toronto, Canada.
Aims: To measure effects between educational attainment and alcohol use as a driver of unequal alcohol-attributable mortality.
Design: Nation-wide cohort study using a longitudinal design, linking data from the 1997-2018 National Health Interview Survey to mortality data of the National Death Index in 2019. The study has an average follow-up time of 10.
Cureus
January 2025
Faculty of Medicine, King Abdulaziz University, Jeddah, SAU.
Objective: Our study aims to assess the clinical effectiveness of using MRI in diagnosing various shoulder pain-related conditions among patients at King Abdulaziz University Hospital.
Methods: 383 patients who were admitted to King Abdulaziz University Hospital and had shoulder magnetic resonance imaging between January 2020 and July 2024 were studied retrospectively. The dataset was subjected to a thorough statistical analysis using descriptive and inferential approaches.
Glob Epidemiol
June 2025
Business Analytics (BANA) Program, Business School, University of Colorado, 1475 Lawrence St. Denver, CO 80217-3364, USA.
AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results. This paper demonstrates the potential of large language models (LLMs), such as ChatGPT, to facilitate statistical analyses, including survival data analyses, for health risk assessments. Through AI-guided analyses using relatively recent and advanced methods such as Individual Conditional Expectation (ICE) plots using Random Survival Forests and Heterogeneous Treatment Effects (HTEs) estimated using Causal Survival Forests, population-level exposure-response functions can be disaggregated into individual-level exposure-response functions.
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