This study aimed to assess among Ukrainian adults: (1) knowledge of mental disorders; (2) attitudes towards people with mental health disorders, and to the delivery of mental health treatment within the community; and (3) behaviours towards people with mental disorders. A cross-sectional survey of Ukrainian adults aged 18–60 was conducted. Stigma-related mental health knowledge was measured using the mental health knowledge schedule. Attitude towards people with mental health disorders was assessed using the Community Attitudes towards Mental Illness scale. The Reported and Intended Behaviour scale was used to assess past and future intended behaviour towards people with mental health disorders. Associations between gender, age, and educational level and the knowledge and attitudes measures were identified. There was evidence of a positive association between being male and positive intended behaviours towards people with mental health disorders [mean difference (MD) = 0.509, 95% confidence interval (CI) 0.021–0.998]. Older age was negatively associated with positive intended behaviours towards people with mental health disorders (MD = −0.017, 95% CI 0.0733 to −0.001). Higher education was positively associated with stigma-related mental health knowledge (MD = 0.438, 95% CI 0.090–0.786), and negatively associated with authoritarian (MD = 0.755, 95% CI 0.295–1.215) attitudes towards people with mental health problems. Overall, the findings indicate a degree of awareness of, and compassion towards, people with mental illness among Ukrainian adults, although this differed according to gender, region, and education level. Results indicate a need for the adoption and scaling-up of anti-stigma interventions that have been demonstrated to be effective.
Mental Health Stigma in Ukraine: Cross-Sectional Survey
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Criminal Justice, Artificial Intelligence Systems, and Human Rights
Završnik, Aleš. “Criminal Justice, Artificial Intelligence Systems, and Human Rights.” ERA Forum 20, no. 4 (March 1, 2020): 567–83.
- Authors with Diverse Backgrounds
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Racial, Skin Tone, and Sex Disparities in Automated Proctoring Software
Yoder-Himes, Deborah R., Alina Asif, Kaelin Kinney, Tiffany J. Brandt, Rhiannon E. Cecil, Paul R. Himes, Cara Cashon, Rachel M. P. Hopp, and Edna Ross. “Racial, Skin Tone, and Sex Disparities in Automated Proctoring Software.” Frontiers in Education 7 (September 20, 2022).
- Authors with Diverse Backgrounds