Technologies to support dementia caregivers are often oriented around key milestones and transitions in the caregiving journey, which may not align with how family caregivers conceptualize and adapt to the everyday realities of care. In this study, we present findings from semi-structured interviews with 15 family caregivers of people with dementia to understand how they perceive their caregiving journeys over time. We find that caregivers live in a constant state of flux, characterized by ongoing, unpredictable, and multidimensional changes and fluctuating care demands throughout their journey. Our findings identify three ways caregivers respond to this constant state of flux: navigating through interdependent, multidimensional changes; iteratively adjusting and abandoning tools and supports; and developing an anticipatory mindset oriented toward future disruptions. We contribute to CSCW and HCI by showing how ongoing, unpredictable fluctuations constitute dementia caregiving and use this orientation as a way of thinking about designing technologies for dementia care.

We investigate the role of large language models (LLMs) in supporting mental health by analyzing Reddit posts and comments about mental health conversations with ChatGPT. Our findings reveal that users value ChatGPT as a safe, non-judgmental space, often favoring it over human support due to its accessibility, availability, and knowledgeable responses. ChatGPT provides a range of support, including actionable advice, emotional support, and validation, while helping users better understand their mental states. Additionally, we found that ChatGPT offers innovative support for individuals facing mental health challenges, such as assistance in navigating difficult conversations, preparing for therapy sessions, and exploring therapeutic interventions. However, users also voiced potential risks, including the spread of incorrect health advice, ChatGPT's overly validating nature, and privacy concerns. We discuss the implications of LLMs as tools for mental health support in both everyday health and clinical therapy settings and suggest strategies to mitigate risks in LLM-powered interactions.