Exploring public perceptions of artificial intelligence in bereavement support: a qualitative study of griefbots

BackgroundArtificial intelligence technologies designed to simulate deceased loved ones, often referred to as griefbots, are emerging as a novel form of bereavement support. Although such technologies may offer emotional comfort and a sense of continued connection with the deceased, they also raise complex psychological, ethical, and societal concerns. This study aimed to explore public perceptions […]

Beyond the digital divide: multi-group SEM examination of socioeconomic status, mHealth utilization, and urban-rural physical activity disparities in Indonesia

BackgroundMobile health (mHealth) technological innovations are now widely being promoted as a scalable solution to the rising problem of obesity. Unfortunately, there is a dearth of empirical research on the extent to which mHealth utilization is associated with acceptance disparities in emerging economies.ObjectiveThis work explores the associations among socioeconomic status (SES), urban-rural differences, mHealth utilization, […]

Explainable and interpretable models for predicting early-onset hypertension in the Tlalpan 2020 cohort

BackgroundEarly-onset hypertension results from complex interactions among demographic, lifestyle, metabolic, and psychosocial factors. While machine learning models can predict hypertension with relative accuracy, their lack of interpretability limits their clinical utility.MethodsUsing a nested case-control design based on the Tlalpan 2020 prospective cohort, a 10-year study of clinically healthy adults in Mexico City, this study applies […]

A qualitative, multi-framework methodology for analysing health information technology–related patient safety incidents

BackgroundThe increasing reliance on health information technology (HIT) has introduced new and often unforeseen risks to patient safety in complex healthcare systems. Many HIT-related safety problems emerge only after systems are embedded in routine clinical practice and are difficult to identify using prospective or purely quantitative methods. Incident reports provide valuable insights into real-world failures, […]

Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles

arXiv:2606.06715v1 Announce Type: cross Abstract: We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label. Using articles from AllSides, paired with shared sentiment annotations from Llama-3.3-70b-versatile, we compare ideology labels from expert human annotators, GPT-4o-mini (baseline and finetuned), and Llama-3.3-70B. We […]

LLM-Guided Search for Deletion-Correcting Codes

arXiv:2504.00613v2 Announce Type: replace Abstract: Finding deletion-correcting codes of maximum size has been an open problem for over 70 years, even for a single deletion. We adapt FunSearch, a large language model (LLM)-guided evolutionary search, to discover functions that construct deletion-correcting codes at short code lengths. For a single deletion, our search finds a function […]

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