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Skip search results from other journals and go to results- 265 Journal of Medical Internet Research
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Exploring Social Media Posts on Lifestyle Behaviors: Sentiment and Content Analysis
In order to further understand the topics communicated on social media and how users’ perceptions are aligned with recommended health practices, lexicon-based sentiment analysis can be supported with manual content analysis. A codebook can be used to manually assign labels to each post, which will provide a more in-depth analysis of the posts [17].
JMIR Infodemiology 2025;5:e65835
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In contrast, our preliminary investigations into quantum-enhanced EEG processing, such as quantum machine learning for enhanced EEG encoding and quantum contrastive learning [35], demonstrate how EEG can serve as the central signal in generative tasks.
JMIR Med Inform 2025;13:e72027
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Although tele–mental health and parent coaching services are increasingly available, it remains unclear which types of support programs are best suited to various populations and how to best deliver these support programs. One population particularly well suited to telehealth support programs is caregivers of children with neurogenetic conditions (NGCs).
JMIR Res Protoc 2025;14:e64360
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The TMIM explains how uncertainty influences information management behaviors [41] and has been widely applied to explore the barriers and motivations behind health information seeking [42-45]. While its application to health misinformation is relatively limited, it may offer valuable insights into how individuals engage with, process, and respond to health misinformation encountered on social media.
JMIR Infodemiology 2025;5:e67464
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Public Versus Academic Discourse on ChatGPT in Health Care: Mixed Methods Study
This study specifically focused on health care, analyzing tweets that discussed the impact of Chat GPT on the health care sector since its inception and examined how Chat GPT interacts with various public health domains, including health care management, public health, digital health, clinical medicine, and nursing science [6-8].
JMIR Infodemiology 2025;5:e64509
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It provides insights into how a particular rate or proportion has been changing annually, that is, whether it has been increasing, decreasing, or remaining stable [23].
AAPC takes into account trends over multiple years, providing a summary measure of the average rate of change per year over a specified period. It gives a more comprehensive view of trends compared to APC, especially when trends are not linear.
JMIR Form Res 2025;9:e55670
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On a scale of 1 to 7 (lower is better), mean ratings were 2.0 (family) and 1.3 (staff) for communication quality for how the video conveyed the resident’s preferences for daily care; 1.9 (family) and 1.2 (staff) for communicating preferences for EOL. Both family and staff reported increased knowledge about resident preferences for daily and EOL care.
J Med Internet Res 2025;27:e71479
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Reference 23: Scientists flock to DeepSeek: how they’re using the blockbuster AI model Reference 25: How China created AI model DeepSeek and shocked the world
J Med Internet Res 2025;27:e69929
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These themes represent the core aspects of the interventions, focusing on how individuals can record, store, recall, and share their personal experiences. These elements collectively contribute to the effectiveness of technology-based storytelling interventions.
Various digital storytelling applications are designed to assist older adults, particularly those with MCI or dementia, in enhancing their mood and increasing social engagement [28].
JMIR Hum Factors 2025;12:e70177
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