Empathic Accuracy Task: Indian Adaptation and Validation

共情准确性任务:印度适应性与验证

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Abstract

BACKGROUND: The Empathic Accuracy Task (EAT) is an objective measure to assess empathic accuracy. Due to the variability in the number and linked emotions of the narrated events, we adapted EAT for the Indian sociocultural setting as Indian EAT (I-EAT). METHODS: Eight videos were adapted in three languages (English, Hindi, and Kannada), narrating emotional events with a uniform representation of age groups, different emotions, and sex. The adapted I-EAT was then validated by cross-sectional comparison with different tests similar to EAT and those that assessed concepts different from or similar to empathy, in 29 healthy young adults, 23 healthy older adults (aged ≥60 years) along with clinical groups of 15 young people with depression, 15 older people with depression, and 15 young people with schizophrenia. RESULTS: We selected eight videos with good content validity and internal consistency (Cronbach's alpha = 0.73. We obtained satisfactory concurrent validity of the EAT scores with the self-reported empathic assessments using the Questionnaire of Cognitive and Affective Empathy (Cognitive empathy score = 0.29, p = .034; Total score = 0.29, p = .035) and Interpersonal Reactivity Index (Empathic concern score = 0.45, p = .001). Good divergent validity was revealed in the high inverse correlation recorded with the Apathy Evaluation Scale ( = -0.67, p < .001). I-EAT did not correlate significantly with measures of social cognition. Known-groups validity was adequate in young adults with the significantly lower EAT scores (Cohen's d: 0.77 to 1.15) in the Schizophrenia group and higher EAT-N scores (Cohen's d: 0.51) in the Depression group, compared to the Healthy group. The Healthy group of the geriatric population also achieved significantly higher EAT scores (Cohen's d: 0.71 to 0.85) than the Depression group. CONCLUSION: With a good validity and internal consistency, I-EAT can be used in the Indian population to assess empathic accuracy without compromising performance of the original EAT.

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