Attitudes towards people with mental disorders: results of a psychometric evaluation and confirmatory factor analysis of the stigma towards people with mental disorders (SToP-MD) scale

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 Stigma Toward People with Mental Disorders scale (SToP-MD) 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 = 448), confirmatory factor analysis supported a two-factor structure comprising prejudiced stigmatization (SToP-MD-PS) and assumption of problems (SToP-MD-AP). The model demonstrated adequate fit according to conventional indices (CFI = 0.97, TLI = 0.96, SRMR = 0.07), although robust indices indicated only moderate fit (robust CFI = 0.91, robust RMSEA = 0.13). Internal consistency was good for the PS subscale (ω = 0.83) but limited for the AP subscale (ω = 0.51). In study 3 (N = 266), the scale's sensitivity to short-term change was examined following exposure to differently framed media content.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 preliminary yet consistent evidence of structural and construct validity, as well as change sensitivity. 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.