Background: #SocialMedia #SoMe #hcsm use is ubiquitous among young people, yet its relationship with #MentalHealth may depend on why it is used. Understanding the motives underlying #SocialMedia #SoMe #hcsm engagement may provide greater insights into its potential risks and benefits than considering overall use alone. Objective: This study aimed to identify latent dimensions of motives for #SocialMedia #SoMe #hcsm use and examine their associations with #anxiety, #depression, well-being, and resilience in a sample of older adolescents. Methods: Cross-sectional data were drawn from the BrainWaves study (N=6820; mean age 16.72, SD 0.74 years). #SocialMedia #SoMe #hcsm use motives were assessed using 10 items and exploratory factor analysis identified 2 latent dimensions: avoidant-escapist use (boredom, addiction, distraction, and procrastination) and social-recreational use (socializing, communication, entertainment, and observation). Confirmatory factor analysis was used to estimate factor scores, which were examined using linear regression. Structural equation modeling estimated associations with #anxiety, #depression, well-being, and resilience, with secondary analyses accounting for gender and deprivation. Results: Linear regression analyses showed that avoidant-escapist motives were associated with higher #anxiety and #depressive symptoms, whereas social-recreational motives were associated with lower symptoms. No interactions with gender or deprivation were observed. The structural equation model showed acceptable model fit (comparative fit index=0.945; root mean squared error of #Approximation=0.057; standardized root mean squared residual=0.054). Avoidant-escapist motives were associated with higher #anxiety (β=0.543) and #depressive symptoms (β=0.629) and lower well-being (β=−0.610) and resilience (β=−0.469). Conversely, social-recreational motives were associated with lower #anxiety (β=−0.228) and #depressive symptoms (β=−0.342) and higher well-being (β=0.362) and resilience (β=0.242;

Sma, Sansone (Centro NeMo): "Terapia orale con risdiplam facilita autonomia"

Background: Mental illness contributes substantially to global disability, and public adoption of AI for #MentalHealth support is accelerating without commensurate safety evaluation. General-purpose large language models can fail to recognize naturalistic text expressing suicidal ideation, with immediate clinical consequences. Domain-specific natural language processing methods offer a contrasting #Approach, but ensembles integrating the two have not been formally characterized. Objective: We aimed to (1) benchmark 5 modeling #Approaches for classifying #MentalHealth–related #SocialMedia #SoMe #hcsm text, (2) develop and optimize ensembles integrating a domain-specific natural language processing classifier with a fine-tuned large language model, and (3) derive a closed-form framework constraining ensemble weighting in safety-critical classification. Methods: We analyzed 60,889 publicly available Reddit and Twitter texts spanning 9 categories (#anxiety, bipolar disorder, #depression, a normal baseline, personality disorder, stress, suicidal ideation, Attention-Deficit/Hyperactivity Disorder #adhd, and #Autism spectrum disorder). Labels were derived from the originating subreddit or a self-stated condition and are not clinical diagnoses. Five architectures were compared: prompt-engineered base GPT-4o-mini; fine-tuned GPT-4o-mini; a domain-specific classifier (support vector machine over symptom-informed lexical features); and 2 ensembles of these models, hybrid probability–indicator fusion and soft probability fusion. Data were split into 70%, 10%, and 20% at the post level (n=42,622, n=6089, and n=12,178); weights were selected on the validation split and all reported performance comes from the held-out test split. Closed-form bounds on permissible language model weights were derived for both strategies. CIs are paired bootstrap intervals (n=10,000 replicates); accuracy differences were tested with exact McNemar tests. Results: The domain-specific classifier achieved 91.9% accuracy (95% CI 91.4%-92.4%), exceeding the base (58.7%) and fine-tuned (88.6%) large language models. Both ensembles outperformed either constituent model, reaching 93.9% (soft fusion; 95% CI 93.5%-94.3%) and 93.8% (hybrid fusion; 95% CI 93.4%-94.2%) at a weighting of 60% classifier to 40% fine-tuned model, selected on validation (P

Background: #SocialMedia #SoMe #hcsm influencers occupy a pervasive role in billions of users’ daily #Digital lives, particularly among adolescents and young adults. Audiences develop parasocial engagement with these figures, including parasocial relationships (PSRs) and parasocial interactions (PSIs). Despite growing concern about their #MentalHealth implications, no prior meta-analysis has quantitatively synthesized this evidence. Objective: This systematic review and meta-analysis aimed to estimate the associations between influencer-directed parasocial engagement and #MentalHealth outcomes, examine prespecified moderators, and evaluate the quality of the existing evidence base. Methods: Seven databases (PsycINFO, Embase, MEDLINE, ERIC, PubMed, Web of Science, and Scopus) were searched from inception. Studies quantitatively assessing PSRs or PSIs with #SocialMedia #SoMe #hcsm influencers and reporting #MentalHealth outcomes were eligible. A 3-level random-effects meta-analysis was conducted using Pearson . Primary pooled estimates were calculated separately for positive or adaptive outcomes, and negative or maladaptive outcomes. Moderators examined included outcome domain, parasocial construct type, age group, gender, cultural region, and platform. Results: Seventeen studies (52 effect sizes) were included. Parasocial engagement was positively associated with both positive (=28; =0.38, 95% CI 0.18-0.55) and negative outcomes (=24; =0.24, 95% CI 0.08-0.38), with positive effects significantly stronger. Well-being showed the largest effects (=0.42), followed by #SocialMedia #SoMe #hcsm addiction (=0.33). PSI demonstrated stronger associations than PSR (=0.56 vs 0.22). Effects were largest among adolescents (=0.44) and in Eastern samples (=0.61 vs 0.29). No evidence of publication bias was detected (Egger =.94; fail-safe N=18,643). Conclusions: Parasocial engagement functions as a psychological double-edged sword, reliably linked to both enhanced well-being and problematic engagement. Positive #MentalHealth outcomes, particularly those involving well-being, were generally stronger and more consistent than negative ones. These findings suggest that parasocial engagement should not be understood as uniformly beneficial or harmful; rather, its psychological meaning depends on the outcome domain, parasocial construct type, developmental stage, and cultural context. Future longitudinal, mechanism-based research with diverse samples is needed to clarify when parasocial engagement is most beneficial or harmful. Trial Registration: PROSPERO CRD420261291637; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261291637

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