Research on the role of character virtues (caring, inquisitiveness and self-control) in fostering engagement, reducing burnout and supporting teacher well-being remains limited, yet findings suggest promising effects. This research explores how these virtues influence teachers’ burnout, self-efficacy, engagement, and well-being. A total of 339 Italian teachers completed measures assessing these variables. Path analysis revealed a positive total effect of virtues on teachers’ engagement and well-being. These virtues influenced engagement and well-being indirectly through burnout and self-efficacy, which mediated their effects on both outcomes. Findings emphasize the potential of virtue-based interventions to reduce burnout and improve both professional and personal outcomes.
The large-scale deployment of generative Large Language Models (LLMs) raises growing concerns about their discursive behavior when responding to value-laden geopolitical and humanitarian prompts. This study examines whether and how widely used LLMs exhibit systematic differences in tone and framing when exposed to identical prompts. Five widely deployed models (ChatGPT, Gemini, Claude, Copilot, and DeepSeek) were queried using ten open-ended prompts in Italian between March and June 2025. Responses were analyzed through a structured coding scheme based on predefined tone and framing categories. Rather than assuming discursive neutrality as an inherent property of language models, this study conceptualizes neutrality as a contextual and operational construct, observable through comparative patterns of discursive positioning. The results indicate that discursive neutrality should not be assumed a priori, as it varies systematically across models and prompting contexts. Distinct and recurrent discursive profiles emerge, reflecting differences in framing strategies, levels of assertiveness, and ethical positioning. The analysis situates these findings within broader discussions on model training regimes, alignment strategies, and design choices, highlighting their implications for accountability, transparency, and governance in AI-mediated communication. Methodological limitations, including the interpretive role of human coders, are explicitly addressed. Finally, the study proposes a structured and reproducible evaluation framework for auditing discursive behavior in generative AI systems, enabling systematic comparison of model-specific discursive profiles in value-laden contexts. Overall, the findings underscore the importance of critical and transparent assessment of generative models when they are deployed in journalism, education, and policy-relevant domains.
This study investigates how the presence of Sustainability Committees correlates with corporate environmental performance, specifically as measured by green investments. Using a unique firm-level database, we explore whether firms with Sustainability Committees outperform other firms in terms of green investments. Namely, we define performance as the likelihood of a firm: (i) having undertaken green investments; (ii) planning future green investments; and (iii) perceiving such investments as competitiveness-enhancing. To address potential endogeneity concerns, we employ an instrumental variables approach. Our results suggest that the presence of a Sustainability Committee is associated with stronger realized and planned green investments by firms. Moreover, the empirical results indicate that its presence is associated with viewing green investments as a strategic driver of competitiveness rather than a mere response to regulatory compliance. Our findings contribute novel insights to this field and could be valuable for board members, managers, investors, and policymakers aiming to advance sustainable development strategies.
The study of spinopelvic alignment in asymptomatic individuals is essential for understanding physiological sagittal balance and establishing reference values for spinal deformity assessment. However, the availability of large datasets of healthy subjects is limited by ethical, logistical, and radiation-related constraints. Artificial intelligence (AI)-based synthetic data generation may represent a promising strategy to overcome these limitations. Full-spine standing radiographs from 123 asymptomatic subjects were retrospectively analyzed. Demographic characteristics and multiple spinopelvic parameters, including pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), lumbar lordosis (LL), thoracic kyphosis (TK), and cervical alignment measures, were recorded. An AI-driven probabilistic Gaussian resampling approach with anatomical constraints was used to generate a synthetic dataset of 10,000 biologically plausible cases. Correlations identified within the synthetic dataset were subsequently validated against the original cohort using Pearson correlation analysis and bootstrap resampling (1,000 iterations). The synthetic dataset preserved the statistical distribution of the original population while substantially increasing analytical power. Significant correlations were identified between PI and PT (PT = 0.34 × PI − 7.2), PI and SS (SS = 0.66 × PI + 7.2), and PI and LL (|LL| = 0.55 × PI + 32.0). These relationships were consistent with previously published anatomical models and remained robust when tested in the original cohort and through bootstrap validation. No significant correlation was observed between PI and TK. A significant association was also identified between T1 slope and cervical lordosis. AI-driven dataset amplification represents a feasible and reproducible approach for investigating spinopelvic relationships in limited clinical cohorts. The combination of synthetic data generation, validation on real-world observations, and bootstrap resampling enables the identification of biologically plausible correlations while minimizing the need for additional imaging studies. This methodology may serve as a valuable exploratory tool in spine research and other fields characterized by limited datasets.
Digital media environments constitute pervasive symbolic and psychological contexts, yet comparatively little is known about how the moral content of widely consumed entertainment narratives relates to subjective well-being and health. We quantify the moral content of popular television series distributed through major streaming platforms and construct regional exposure measures by combining narrative moral profiles with subnational Google Trends popularity and streaming-platform penetration. These exposure measures are linked to individual-level outcomes from the World Values Survey covering 71 regions across five countries. Using multilevel models, we examine whether exposure to morally salient entertainment environments is associated with subjective well-being and self-assessed health. Greater exposure to morally salient television narratives is associated with lower life satisfaction and poorer self-assessed health. These associations are robust to a rich set of socio-demographic and socio-economic controls and are not fully explained by material deprivation or family background. Rather than identifying causal media effects, the study introduces an exposure-based framework for examining how symbolic entertainment environments correlate with perceived agency and well-being in contemporary digital ecosystems. More broadly, the study contributes to emerging efforts to integrate computational narrative analysis into research on subjective well-being and population health by conceptualizing entertainment media as ambient symbolic contexts rather than merely as sources of screen time or information exposure.
