KImAge - AI-supported systematization of views on aging in central domains of everyday life

Background: Negative views on aging are linked to discrimination (e.g. in working life or healthcare) and reduced health outcomes. Despite well-documented effects, there is a lack of effective methods to assess views on aging in daily experience. Objectives and Research questions: A photography-based method (Klusmann, 2023; Klusmann & Schüz, 2024) will be used to capture views of aging in everyday life. An AI model will be trained offline to enable a large-scale systematization of photographs based on a category system developed by an expert panel. This will help to estimate the occurrence and relative prevalence of views on aging in daily life of different age groups, in rural and urban areas as well as in settings in which age discrimination is potentially harmful (working life, healthcare). Participants: n = 1,200 German-speaking participants (aged 18+ years) are planned to be enrolled for the study. Study method: Participants will upload their own photographs on an online database hosted by Hetzner Online GmbH. The aim is to generate, analyze and systematize these views on aging captured by photographs. A mixed-methods approach will combine expert-led qualitative coding (development of a category system with coding scheme) with AI-based image classification (offline AI-model being trained based on the coding scheme and then being applied to the full data set). A quantitative content analysis will be used to determine descriptive frequencies and possible differences in age groups, in rural and urban areas as well as in settings in which age discrimination is potentially harmful (working life, healthcare).