The introduction of information and communication technologies (ICT) in elementary education has radically changed the way of teaching and learning in recent decades. In addition, the recent acceleration in the use of online learning due to the pandemic period has increased the reliance on these teaching methods. Online learning has become a popular teaching practice in recent years in parallel with traditional teaching, yet there are still few studies in the literature that delve into a comparison between the two learning situations and in particular studies focusing on these methods within the elementary education are scarce. The purpose of this study is to examine the characteristics of student-to-student and student-to-teacher interactions that occur in online learning as compared to typical face-to-face classroom interactions, with a specific focus on the elementary school level. The quality of teacher-student interactions was measured using the Classroom Assessment Scoring System (CLASS; Pianta et al., 2008). Elementary students were selected from 36 classes ranging from second to fifth grade. The total sample included 769 students (48% male). The majority of teachers were female (N = 31, 86%). The study shows that online teaching can have positive or negative effects on the quality of interaction between teachers and students, depending on the dimensions considered and the age of the participants. This study helps to highlight the differences and specificities of interaction and learning processes in the two situations (online and face-to-face): online teaching seems to improve positive climate, behavior management, and didactic learning formats, while face-to-face teaching seems to provide greater teacher sensitivity and better-quality feedback.

Victimization through hate speech represents identity-based violence during adolescence and is negatively related to core beliefs about justice. However, there is limited information on how exposure to hate speech leads to changes in an adolescent's Personal Belief in a Just World (PBJW). This study used the frameworks of Trauma Theory and Just World Theory to examine a serial mediation model in which victimization through hate speech was expected to lead indirectly to lower PBJW through perceived unsafety at school and decreased school well-being. A sample of 1,108 adolescents (Mage = 15.59; 61% female) in Italy completed self-report measures of victimization through hate speech, perceived school unsafety, school well-being, and PBJW. Results obtained using PROCESS Model 6 with 5,000 bootstrap samples indicated a significant indirect relationship: exposure to hate speech was associated with greater perceptions of unsafety in the school environment, which in turn were associated with lower levels of school well-being and lower PBJW. The direct effect of hate speech on PBJW was not significant, suggesting that the psychological impact of hate speech is primarily a result of how adolescents experience changes in their school environment on a daily basis. These results illustrate the cognitive pathway through which exposure to hate speech may contribute to adolescents' internalization of personal justice beliefs. This study advances understanding of the associations among hate speech victimization, school climate perceptions, and adolescents' personal beliefs about justice by identifying a theoretically derived pathway linking these constructs.

Large language models (LLMs) may support realistic virtual patients, yet most existing systems rely on top-down construction approaches, such as manually curated vignettes. We investigated a bottom-up alternative in which social media histories were transformed into virtual personas examining stability and alignment with human judgment. Histories from Italian Reddit users reporting depressive or anxiety-related distress were manually anonymized and synthesized into two prompt variants (Base and Clinically Enriched). GPT-4o and DeepSeek-V4-Pro completed the PHQ-9 and GAD-7 in independent generations per persona, prompt, model, and scale. Stability was assessed using single-generation and aggregated intraclass correlations. Human alignment with symptom ratings from a psychologist was examined using persona-level correlations and item-level area under the curve (AUC). PHQ-9 total scores showed high single-generation stability across conditions (ICC(2,1) = .84–.88), whereas GAD-7 stability varied by model. Aggregating generations yielded high reliability in all conditions, although item-level stability was heterogeneous. Human alignment was stronger for PHQ-9 than GAD-7 and was highest for DeepSeek-V4-Pro with the Base prompt (r = .98). DeepSeek-V4-Pro also showed higher item-level discrimination, while clinical enrichment had scale-dependent effects. Our findings support the feasibility of bottom-up persona construction, while highlighting the need for psychometric evaluation across models and symptom domains.

Risky alcohol consumption is a major public health concern, yet significant barriers exist to effective screening. The present study examines the potential of Large Language Models (LLMs) to infer risky alcohol use from social media text. The unobtrusive nature of this approach could provide a more scalable way to assess alcohol risk in large populations. To this aim, we analyzed Facebook status updates from 208 adults from Italy (mean age = 26.8, 70.7 % female) who also completed the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C), a brief validated self-report measure of risky drinking. Two state-of-the-art LLMs, Gemini 1.5 Pro and GPT-4o, were used to assess alcohol risk and to quantify alcohol references. Results demonstrated strong inter-model agreement between risk inferences (ρ = 0.572, p < 0.001). LLM-inferred risk scores showed moderate correlations with AUDIT-C scores (Gemini 1.5 Pro: ρ = 0.344, p < 0.001; GPT-4o: ρ = 0.375, p < 0.001; Average: ρ = 0.405, p < 0.001). These correlations were significantly stronger among participants with recent posts (Average risk score: ρ = 0.500, p < 0.001) than among those without (ρ = 0.294, p = 0.008). The strongest correlation was observed between average LLM-inferred risk scores and AUDIT-C in the recent posts group (disattenuated ρ = 0.606). These findings suggest that LLMs offer a promising tool for identifying risky alcohol use when analyzing recent social media activity. Their accuracy is comparable to some traditional alcohol assessment methods, highlighting their potential to enhance early detection efforts. Limitations and future research directions are discussed.

This study investigated the feasibility of using large language models (LLMs) to infer problematic Instagram use, which refers to excessive or compulsive engagement with the platform that negatively impacts users’ daily functioning, productivity, or well-being, from a limited set of metrics of user engagement in the platform. Specifically, we explored whether OpenAI’s GPT-4o and Google’s Gemini 1.5 Pro could accurately predict self-reported problematic use tendencies based solely on readily available user engagement metrics like daily time spent on the platform, weekly posts and stories, and follower/following counts. Our sample comprised 775 Italian Instagram users (61.6% female; aged 18–63), who were recruited through a snowball sampling method. Item-level and total scores derived by querying the LLMs’ application programming interfaces were correlated with self-report items and the total score measured via an adapted Bergen Social Media Addiction Scale. LLM-inferred scores showed positive correlations with both item-level and total scores for problematic Instagram use. The strongest correlations were observed for the total scores, with GPT-4o achieving a correlation of r = 0.414 and Gemini 1.5 Pro achieving a correlation of r = 0.319. In cross-validated regression analyses, adding LLM-generated scores, especially from GPT-4o, significantly improved the prediction of problematic Instagram use compared to using usage metrics alone. GPT-4o’s performance in random forest models was comparable to models trained directly on Instagram metrics, demonstrating its ability to capture complex, non-linear relationships indicative of addiction without needing extensive model training. This study provides compelling preliminary evidence for the use of LLMs in inferring problematic Instagram use from limited data points, opening exciting new avenues for research and intervention.

Advances in artificial intelligence, particularly in natural language processing, offer promising tools for addressing mental health challenges in online contexts, potentially identifying at-risk individuals and informing timely interventions. This study investigates the potential of Large Language Models (LLMs) for automatically triaging social media posts expressing psychological distress. Using a dataset of 425 Italian-language Reddit posts, we compared the triage performance of three state-of-the-art LLMs - ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro - with trained clinician assessment using an adapted version of the Mental Health Triage Scale (MHTS), a validated instrument used in psychiatric screening services. A zero-shot prompting approach, with and without role assignment (simulating a clinician's perspective), evaluated the models' capability to assess intervention urgency. Results revealed that LLMs consistently overestimated urgency compared to human raters, although correlations with human judgments were moderate to strong, with GPT-4o and Claude 3.5 Sonnet demonstrating higher agreement. GPT-4o achieved the best classification performance, highlighting its potential for this task. Claude 3.5 Sonnet showed high sensitivity but lower precision, indicating a tendency toward false positives, while Gemini 1.5 Pro exhibited more balanced but generally lower performance. These findings suggest that while LLMs show promise for mental health triage in social media, their tendency to overestimate urgency and model-specific variations in performance underscore the need for careful interpretation, and human oversight when applying LLMs in mental health contexts.

In the era of the mobile Internet, online groups, which often serve as a primary form of communication and socialization and provide support within personal networks, have become an important cyberspace for adolescents. While the prevalence of being kicked out from online groups is relatively high, the influence of such online exclusion and its underlying mechanisms have been less studied. This study examined the relationship between being kicked out (BKO) from WhatsApp groups and students' school achievement was explored. Valid participants were 858 students (Mage = 14.01 years, SDage = 2.23) from middle schools and high schools. A negative relationship was found between BKO and students' grade point average. Fear of missing out, problematic social media use, and internalizing symptoms played mediating roles in this association. This study may provide valuable insights for programs aimed at improving students' academic performance and well-being.

INTRODUCTION:Large language models (LLMs) offer a promising approach to infer personality traits unobtrusively from digital footprints. However, the reliability and validity of these inferences remain underexplored. METHOD:Gemini 1.5 Pro and GPT-4o were used to infer Big Five traits from 2 years of Facebook posts by 1214 Italian users. Predictions were compared to self-reports on the Ten-Item Personality Inventory. RESULTS:LLM predictions underestimated Agreeableness and Conscientiousness, overestimated Extraversion, while Neuroticism and Openness closely aligned with self-report means. On repeated prompting, Gemini 1.5 Pro inferences showed less variability than GPT-4o, with both models achieving excellent reliability when aggregating inferences. Temporal stability was highest when combining predictions across LLMs, with test-retest correlations over 2 years ranging from 0.44 for Conscientiousness to 0.60 for Openness. Cross-LLM agreement was highest when combining inferences from multiple time points, with correlations ranging from 0.58 for Neuroticism to 0.83 for Extraversion. Correlations with self-reports were modest, reaching 0.27 for Extraversion, 0.24 for Agreeableness, 0.23 for Conscientiousness, 0.18 for Neuroticism, and 0.31 for Openness when combining LLM inferences across LLMs and time points. CONCLUSION:These findings advance understanding of LLMs' potential for personality inference, highlighting the importance of aggregating inferences to enhance the reliability and validity of such assessments.

Research on the relationship between social media use and psychological well-being has yielded mixed findings, underscoring the need for more nuanced investigations. While some studies have examined public social media feedback (e.g., likes), the psychological implications of private feedback mechanisms remain underexplored. This cross-sectional study investigates how frequent checking of Instagram Stories’ views list relates to psychological well-being indicators – namely, loneliness, depression, life satisfaction – using an online survey with 694 Italian Instagram users (Mean age = 25.6, SD = 6.95; 62.2

Background: Childhood traumatic experiences can profoundly impact individuals, posing risks to the physical and psychological well-being of children and influencing their psychological development. Teachers in primary schools play a critical role in identifying and reporting suspected cases of child abuse and maltreatment (CAM), which initiates child protection interventions. However, the psychological factors that influence teachers' likelihood of reporting suspected CAM cases remain largely unexplored. Aim: This study investigates the influence of teachers' childhood traumatic experiences and psychological factors (i.e., cognitive empathy and psychological detachment) on their reporting behavior regarding child abuse and maltreatment, addressing an important social issue. Participants: The study involved 1380 primary school teachers from Italy (88.3 % female; aged 21-69, Mage 46.7, DS 10.3). Results: The results reveal that teachers with a history of childhood emotional abuse tend to report a higher number of suspected child abuse and maltreatment cases. Other forms of traumatic childhood experiences were not significantly associated with teachers' reporting suspected cases of CAM. Additionally, cognitive empathy and psychological detachment emerge as significant predictors of teachers' reporting behavior. Conclusions: This research contributes to the existing literature by providing unique insights into actual reporting behavior within an unexplored cultural context.

Building on recent findings by Fournier and colleagues (2023), the present study examined the fit of a bi-dimensional model of problematic Instagram use, distinguishing between non-pathological high engagement and problematic symptoms mirroring addictive tendencies. A sample of 696 Italian adults completed an online survey assessing problematic Instagram use, personality traits, psychological distress, usage motives for Instagram use, and Instagram usage metrics. Confirmatory factor analysis supported the bi-dimensional model, with high engagement (salience and tolerance) and problematic symptoms (relapse, withdrawal, conflict, and mood modification) as distinct factors. Neuroticism, depression, emotional dysregulation, loneliness, and FoMO and the diversion motive were more strongly correlated with problematic symptoms. In turn, social interaction, documentation, and self-promotion were more associated with high engagement. Frequency of sharing posts and stories were also more strongly correlated with high engagement. These findings highlight the importance of distinguishing between high engagement and addiction-like symptoms in understanding problematic Instagram use and inform the development of targeted interventions.

Early adolescents are increasingly engaged in visually rich social media platforms, which may lead to the involvement in visual cybervictimization, i.e., the unsolicited sharing of personal visuals, resulting in negative mental-health outcomes. The present study examined the association between social media use and suicidal ideation among early adolescents, with a focus on the mediating roles of visual cybervictimization and internalizing symptoms. The sample consisted of 1140 middle-school students from Northwestern Italy with a mean age of 12.35 years (SD = 0.97), 53.3

The present study explored how sharing verbal status updates on Facebook and receiving Likes, as a form of positive social feedback, correlate with current and perceived changes in Quality of Life (QoL). Utilizing the Facebook Graph API, we collected a longitudinal dataset comprising status updates and Likes received by 1577 adult Facebook users over a 12-month period. Two monthly indicators were calculated: the percentage of verbal status updates and the average number of Likes per post. Participants were administered a survey to assess current and perceived changes in QoL. Confirmatory Factor Analysis (CFA) and the Auto-Regressive Latent Trajectory Model with Structured Residuals (ALT-SRs) were used to model longitudinal patterns emerging from the objective recordings of Facebook activity and explore their correlation with QoL measures. Findings indicated a positive correlation between the percentage of verbal status updated on Facebook and current QoL. Online positive social feedback, measured through received Likes, was associated with both current QoL and perceived improvements in QoL. Of note, perceived improvements in QoL correlated with an increase in received Likes over time. Results highlight the relevance of collecting and modeling longitudinal Facebook data for the investigation of the association between activity on social media and individual well-being.

Instagram is one of the most used platforms, and ephemeral stories are proving to be the most used medium for users to share content on the platform. However, there have been few studies examining this type of content in relation to emotional well-being. This study examined the association between the number of published Instagram stories, psychological well-being, personality traits, and gender in a sample of 734 Instagram users from Italy, including 281 men and 453 women, with a mean age of 25.19 years (SD = 7.08). Participants were recruited online and asked to complete an online questionnaire. Differences were found between genders in terms of time spent on Instagram, but not in terms of the number of stories posted in the past week. In the overall sample, a small positive correlation was found between the number of Instagram stories posted and extraversion. When considering gender differences, small effect sizes were observed for emotional dysregulation, agreeableness, and neuroticism, indicating a stronger association with Instagram stories in the female group, and for openness, indicating a stronger association in the male group. Results of multiple regression analyses suggest that among females, psychological variables, including personality and emotional distress, may have a stronger association with Instagram stories. To our knowledge, this is the first study to report these differences. The findings help to clarify how certain characteristics of social media platforms relate to psychological well-being and personality differently in men and women in their journey to using social media.

Attendance at preschool represents an important transition, as it is often here that children have their first experience with unfamiliar adults. In this context, several factors can affect children’s adjustment. Two important protective factors are the attachment relationship with the teacher and the level of executive functions. We investigate this interaction in order to reduce the occurrence of internalizing and externalizing symptoms. The results show that both the quality of the attachment relationship with the teacher and executive functions are related to a lower occurrence of internalizing and externalizing behaviours, and that executive functions play a mediating role between the attachment to the teacher and children’s behaviours. Therefore, from the perspective of promoting children’s adjustment to preschool and, more generally, children’s well-being, it is critical at this age to promote the development of executive functions within a secure attachment relationship with the preschool teacher.

The present study investigated the postdictive validity of self-report Big Five personality traits using over ten years of recording of online behaviors, namely Facebook Page Likes. We explored how personality traits correlate with interests and preferences expressed through Facebook Likes recorded up to ten years before the personality assessment and examined the consistency of these correlations over time. The recruited sample consisted of 601 adult Facebook users, predominantly young adults, with 73.70 % female and 26.30 % male participants. Facebook Page Likes data were analyzed using topic modeling techniques to extract meaningful indicators of individual difference in user interests. Findings revealed significant associations between personality traits and participants' interests as expressed using Likes over ten years of online activity. Conscientiousness showed consistent negative correlations with leisure and entertainment interests. Openness to Experience positively correlated with interests in artistic and cultural fields, including non-profit organizations, theaters, musicians, and entertainment and media. Extraversion demonstrated positive correlations with social entertainment, such as nightclubs and restaurants. Agreeableness and Emotional Stability did not show significant average associations. There was a negative correlation between the number of Likes and Conscientiousness, suggesting that individuals that are more conscientious express fewer Page Likes. Conversely, a positive correlation existed between Page Likes and Openness. Overall, correlations were small but mostly consistent over time, although correlations with the Openness trait suggested a stronger association with more recent interests. This research underscores the enduring influence of personality on online behaviors, including activity on social media.

According to the perspective of multiple attachments, children establish significant relationships with individuals outside their family, such as teachers and peers. The aim of this research was to observe which dimensions of attachment behaviors that preschoolers show toward their teachers are associated with greater social preference and social impact regarding peers. Research Findings: This study involved 261 children in preschool (49.0% female; age: M = 49.6 months; SD = 9.7) and their teachers. Independent observers assessed the children using the Italian version of the Attachment Q-Sort (AQS). Furthermore, peer social preference and social impact were measured using the peer nomination technique. The results indicate an association between children’s organization of attachment behaviors with teachers and social preference. In general, our data support the idea that children’s attachment behavior toward their teacher is associated with both social preference and social influence. Specifically, the types of attachment behavior that are more strongly associated with social preference and social affect are those related to avoidance, positive negotiation, and difficult negotiation with the teacher. Practice and Policy: This paper also discusses the results from a theoretical point of view along with the research limitations, directions for future investigations, and possible interventions.

This study examined the mechanism of effect of the WHO Caregiver Skills Training (CST) through secondary analysis of a pilot RCT conducted in community settings. Participants were 86 caregivers (77% mothers) of children with ASD (78% male, mean age: 44.8 months) randomized to CST (n = 43) or treatment as usual (n = 43). The primary outcomes, measured at baseline (t1), immediately post-intervention (t2), and 3 months post-intervention (t3), were derived from the coding of caregiver-child free play interactions with the Brief Observation of Social-Communication Change (BOSCC) and the Joint Engagement Rating Inventory scale (JERI). At t3 positive treatment main effects had been observed for caregiver skills supportive of the interaction and for flow of the interaction (JERI), albeit only non-significant changes in the expected direction for child outcomes: autism phenotypic behaviors (BOSCC), joint engagement and availability to interact (JERI). This study tested the theory of change of CST, hypothesizing that the intervention would lead to an improvement on all child and dyad outcomes through an increase in the caregiver skills supportive of the interaction. Serial mediation analyses revealed that the effect of the intervention was significantly influenced by change in caregiver skills. Participation in the intervention led to notable increases in caregiver skills at t2 and t3, which subsequently contributed to improvements at t3 in flow of the interaction, autism phenotypic behavior, joint engagement, and availability to interact. We confirmed our a priori hypothesis showing that change in caregiver skills significantly mediated the effect of treatment on the dyad primary outcome, as well as on the other child outcomes that had shown non-significant changes in the expected direction. Implications for intervention design and policy making in the context of public health services are discussed.

Research has highlighted the importance of the quality of the teacher-student relationship in fostering more positive developmental and academic outcomes for students. The aim of the study is to validate an Italian short form of the Student Perception of Affective Relationship with Teacher Scale (SPARTS) in an Italian cultural context. A sample of 1,700 students aged 8-15 years (F=47.8%; mean age =10.25, SD=1.38) was recruited from 24 Italian public schools. They completed an anonymous questionnaire that included the SPARTS, sociodemographic data, grade point average, and a measure of psychological adjustment. Our data suggest that the Italian version of the SPARTS has good psychometric properties. In particular, our study provides evidence for the validity of the two-factor model SPARTS (i.e., closeness and conflict dimensions). In addition, our study demonstrates adequate internal consistency of the two subscales (closeness and conflict) and criterion validity of the subscales; specifically, closeness to the teacher is positively related to prosocial behaviors and positive academic outcomes, whereas closeness has a negative correlation with emotional and behavioral symptoms and difficulties. In contrast, our results suggest that the conflict dimension is negatively correlated with prosocial behaviors and academic achievement and positively correlated with emotional symptoms and behavioral difficulties. Our data suggest the usefulness of SPARTS as a measure to assess the quality of student-teacher relationships.

Abstract This study delves into the critical issue of suicidal ideation among early adolescents aged 10 to 14, where suicide ranks as the second leading cause of death globally. Suicidal ideation is closely linked to other psychological distress indicators and high-risk behaviors, particularly among early adolescents. These individuals are also vulnerable to cyberbullying, and the rise of visual social media platforms such as Instagram, TikTok, Snapchat, and YouTube has transformed their social interactions. The study involved 1143 middle-school students in Northern Italy with a mean age of 12.34 (SD = 0.97; females 53.2%), using a self-report questionnaire to gather data on social media usage, bullying behaviors, and suicidal ideation. Statistical analyses included Spearman rank-order correlations, Shapiro-Wilk test, Kruskal-Wallis tests, Fisher's exact tests, and mediation models. The findings revealed significant correlations between daily use of specific visual social media platforms like TikTok and Instagram was positively associated with both visual cyberbullying and suicidal ideation. Gender differences were observed in the associations, and mediation analysis confirmed that the relationship between social media use and suicidal ideation was mediated by involvement in visual cyberbullying episodes.This study underscores the intricate relationships between visual social media use, visual cyberbullying, and suicidal ideation among early adolescents. It emphasizes the responsibility of social media platforms in creating safer digital environments for young users.