Geänderte Inhalte

Stigmatizing attitudes toward individuals with mental disorders represent a major barrier to treatment, recovery, and social inclusion. The present research introduces and psychometrically evaluates the German-language SToP-MD (Stigma Toward People with Mental Disorders) scale across three independent studies with distinct samples.In study 1 (N = 266), an initial item pool was developed and refined based on theoretical frameworks and exploratory factor analysis. In study 2 (N = 488), confirmatory factor analysis supported a two-factor structure comprising prejudiced stigmatization (SToP-MD-PS) and assumption of problems (SToP-MD-AP). The model showed acceptable fit (e.g., CFI = .918, TLI = .892, RMSEA = .078, SRMR = .051) and good internal consistencies (α = .84 and α = .78). In study 3 (N = 266), convergent and discriminant validity were examined via Spearman correlations with established instruments.As hypothesized, the SToP-MD subscales were positively associated with depression stigma (DSS) and social distance (SDI), and negatively correlated with openness and agreeableness (NEO-FFI), supporting convergent validity. Discriminant validity was partially confirmed by low or non-significant correlations with attitudes toward physically disabled individuals (ATDP), suicide-related cognitions (CCSS), and socially desirable responding (BIDR).Across all three studies, the SToP-MD demonstrated robust psychometric properties. It captures both overt prejudices and implicit burden assumptions, offering a nuanced assessment of public stigma toward mental disorders. The scale can serve as a valuable tool in stigma research, public health monitoring, and evaluation of interventions. Future research should extend validation to more diverse samples and test predictive and longitudinal utility.

Synthesized speech technology holds potential for enabling natural conversations between humans and machines, particularly in social robotics. However, the combination of synthesized speech with social robots still lacks some qualities of natural speech, which is crucial for human robot interactions, especially for children. In this study, we recorded the neural activity of 5-year-old, typically developing children from middle to high socio-economic households using EEG while they listened to stories narrated by either an adult or a social robot, specifically Furhat. We measured cortical speech tracking to compare how well children's brains tracked synthesized speech from a robot compared to natural speech from an adult. Our results suggest that children do indeed show cortical speech tracking in both scenarios. The results also suggest that cortical speech tracking requires larger time delays between the speech and the response to reach its peak in child-robot interaction compared to child-adult interaction. Possible sources of these differences along with their implications are discussed.

Dr. rer. nat. Dipl. Psych. Mira Preis