Left-Wing Authoritarianism (LWA) has received increasing attention in recent years, yet research on the construct remains limited by the scarcity of instruments validated across different cultures and across the lifespan. Although multidimensional measures of LWA have recently been proposed, no validated Brazilian-Portuguese instrument is currently available. The present study aimed to develop and validate a short Brazilian-Portuguese measure of LWA for use with both adults and adolescents. Drawing on a multidimensional conceptualization, the proposed instrument assessed three LWA dimensions: Anti-Hierarchical Aggression (support for coercive action against dominant groups), Anti-Conventionalism (rejection of traditional norms), and Top-Down Censorship (support for authorities restricting unacceptable views). In Study 1, 764 adults completed the original 39-item LWA instrument. Exploratory Structural Equation Modeling was used to identify the three highest-loading items from each dimension, resulting in a 9-item version of the scale. In Study 2, the resulting instrument was administered to 308 adolescents, and its three-factor structure was evaluated through Confirmatory Factor Analysis. Results supported the proposed three-dimensional structure across both studies, as the abbreviated measure demonstrated adequate factor loadings and internal consistency estimates. Evidence for criterion validity was also observed across both studies. All LWA dimensions were negatively associated with Right-Wing Authoritarianism and Social Dominance Orientation, and positively associated with left-wing political self-categorization. In Study 1, the LWA dimensions were also negatively associated with prejudice toward dissident and derogated groups and positively associated with pro-environmental attitudes. The findings thus provide evidence for the reliability and validity of a brief multidimensional measure of LWA in the Brazilian context.
Facial Emotion Recognition (FER) is a core component of social cognition, supporting theory of mind processes, empathy, and social reasoning. Deficits in FER are associated with functional impairments across multiple domains of typical and atypical development. Measurement instruments with sound psychometric properties are therefore essential to support clinical assessment in different contexts. To present the development process of the “Facial Emotion Recognition Task” (TAREFE), a computer-based test designed to assess FER in Brazilian adults, with emphasis on the methodological procedures and the initial psychometric indicators. A set of stimuli depicting Brazilian faces was used (n = 170, with diversity in sex and racial background). The stimuli represented the six basic emotions and a neutral expression and had been previously validated in a general population sample. Stimulus selection was based on Item Response Theory (1PL and 2PL), with analysis of infit and outfit indices, S-X2 analysis, item difficulty (b) and discrimination (a). Twenty-eight stimuli were selected, showing excellent discrimination (46
Restrictions imposed during the COVID-19 pandemic have raised concern among suicidology and mental health researchers regarding a possible increase in suicide risk, particularly among people already in psychological distress. Attachment style is one relational characteristic that may act as either a vulnerability or a protective factor. To analyze the association between attachment style and suicide risk among patients undergoing online psychotherapy. Additionally, it sought to explore the influence of depression on this relationship and to estimate the prevalence of suicide risk. Cross-sectional analysis of a multi-center sample of patients in online psychotherapy during the COVID-19 pandemic (n = 94) with complete data on all instruments analyzed. Measures included sociodemographic data, attachment style (Adult Attachment Scale-R, AAS-R), depression (PHQ-9), and suicide risk (PHQ-9-item9). Group comparisons used the Mann-Whitney U test; associations used the chi-square test, and relationships between scores used Spearman correlations. The prevalence of higher suicide risk was 43.6
Trait anxiety is common in early adolescence and is linked to poorer academic and psychosocial functioning. Emotional intelligence has been proposed as a protective factor, but evidence suggests that different emotional components may show distinct positive or negative associations with anxiety. This study investigated whether a global, self-reported indicator of emotional intelligence relates to trait anxiety through three self-perceived emotional processes—attention to feelings, emotional clarity, and emotional repair—and whether these pathways differ by sex. A cross-sectional correlational design was implemented with 743 Spanish adolescents aged 12–15 years (46.0
The current conceptualization and measures of artificial intelligence (AI) dependency are generally addiction-based and symptom-centered, having been directly established using the criteria framework of behavioral addiction via the confirmatory approach. Few studies have addressed the non-addictive and process-oriented aspects of AI dependency among university students in the school learning context. To develop a process-centered scale to measure the AI dependency of university students specifically in the school learning contexts from the non-addictive perspective. Three sub-studies were completed. In sub-study 1, an open-ended interview with 17 interviewees revealed that AI dependency in the school learning contexts was commonly understood to be a non-addictive phenomenon. In sub-study 2, the University Student AI-Dependency Scale (USAIDS) was developed based on the non-addictive definition and process-centered characteristics of AI dependency. An exploratory factory analysis of a sample of 442 university students identified three factors: excessive trust in AI (ETAI), knowledge-retrieval dependence on AI (KRDAI), and knowledge-generation dependence on AI (KGDAI). This tri-factor structure of the USAIDS was confirmed in a subsequent confirmatory factor analysis of a sample of 1,804 university students. Furthermore, error variance invariance of the USAIDS held across gender, educational level, and major type. Finally, in sub-study 3, it was confirmed that university students’ AI dependency had weak negative correlations with academic self-efficacy, and critical thinking, but positive correlations with learning burnout, anxiety, and depression. The USAIDS showed adequate psychometric properties among university students in the school learning context. The current study sheds light on understanding the AI dependency of university students in the school learning contexts and appeals to scholars to pay more attention to non-addictive rather than addictive AI dependency among university students.
