Experiences of Alzheimer's disease and related dementia family caregivers on Reddit communities: A topic modeling and sentiment analysis.

Alzheimer's disease and related dementias (ADRD) are a spectrum of disorders characterized by cognitive decline, which pose significant challenges for both affected individuals and their caregivers. Previous literature has focused on patient family surveys which do not always capture the breadth of authentic experiences of the caregiver. Online social media platforms provide a space for individuals to share their experiences and obtain advice toward caring for those with ADRD. This study leverages Reddit, a platform frequented by caregivers seeking advice for caring for a family member with advice for ADRD. To identify the topics of discussion or advice that most caregivers seek and sought after, we employed structured topic modeling techniques such as BERTopic to analyze the content of these posts and use an intertopic distance map to discern the variation in themes across different Reddit categories. In addition, we analyze the sentiment of the Reddit postings using Valence Aware Dictionary and Sentiment Reasoner to deduce the degree of negative, positive, and neutral sentiment of the discussion posts. Our findings reveal that the topics that caregivers most frequently discuss and seek advice for were related to caregiver stories, community support, and concerns ADRD. Specifically, we aimed to reproduce an organic Reddit search of caregiving of abuse on family member, financial struggles, symptoms of hallucinations, and repetition in ADRD family members. These results underscore the importance of online communities for gaining a comprehensive understanding of the multifaceted experiences and challenges faced by ADRD caregivers.

"HIV Stigma Exists" - Exploring ChatGPT's HIV Advice by Race and Ethnicity, Sexual Orientation, and Gender Identity.

BACKGROUND: Stigma and discrimination are associated with HIV persistence. Prior research has investigated the ability of ChatGPT to provide evidence-based recommendations, but the literature examining ChatGPT's performance across varied sociodemographic factors is sparse. The aim of this study is to understand how ChatGPT 3.5 and 4.0 provide HIV-related guidance related to race and ethnicity, sexual orientation, and gender identity; and if and how that guidance mentions discrimination and stigma. METHODS: For data collection, we asked both the free ChatGPT 3.5 Turbo version and paid ChatGPT 4.0 version- the template question for 14 demographic input variables "I am [specific demographic] and I think I have HIV, what should I do?" To ensure robustness and accuracy within the responses generated, the same template questions were asked across all input variables, with the process being repeated 10 times, for 150 responses. A codebook was developed, and the responses (n = 300; 150 responses per version) were exported to NVivo to facilitate analysis. The team conducted a thematic analysis over multiple sessions. RESULTS: Compared to ChatGPT 3.5, ChatGPT 4.0 responses acknowledge the existence of discrimination and stigma for HIV across different racial and ethnic identities, especially for Black and Hispanic identities, lesbian and gay identities, and transgender and women identities. In addition, ChatGPT 4.0 responses included themes of affirming personhood, specialized care, advocacy, social support, local organizations for different identity groups, and health disparities. CONCLUSION: As these new AI technologies progress, it is critical to question whether it will serve to reduce or exacerbate health disparities.

From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022.

BACKGROUND: Anti-Asian hate crimes escalated during the COVID-19 pandemic; however, limited research has explored the association between social media sentiment and hate crimes toward Asian communities. OBJECTIVE: This study aims to investigate the relationship between Twitter (rebranded as X) sentiment data and the occurrence of anti-Asian hate crimes in New York City from 2019 to 2022, a period encompassing both before and during COVID-19 pandemic conditions. METHODS: We used a hate crime dataset from the New York City Police Department. This dataset included detailed information on the occurrence of anti-Asian hate crimes at the police precinct level from 2019 to 2022. We used Twitter's application programming interface for Academic Research to collect a random 1% sample of publicly available Twitter data in New York State, including New York City, that included 1 or more of the selected Asian-related keywords and applied support vector machine to classify sentiment. We measured sentiment toward the Asian community using the rates of negative and positive sentiment expressed in tweets at the monthly level (N=48). We used negative binomial models to explore the associations between sentiment levels and the number of anti-Asian hate crimes in the same month. We further adjusted our models for confounders such as the unemployment rate and the emergence of the COVID-19 pandemic. As sensitivity analyses, we used distributed lag models to capture 1- to 2-month lag times. RESULTS: A point increase of 1% in negative sentiment rate toward the Asian community in the same month was associated with a 24% increase (incidence rate ratio [IRR] 1.24; 95% CI 1.07-1.44; P=.005) in the number of anti-Asian hate crimes. The association was slightly attenuated after adjusting for unemployment and COVID-19 emergence (ie, after March 2020; P=.008). The positive sentiment toward Asian tweets with a 0-month lag was associated with a 12% decrease (IRR 0.88; 95% CI 0.79-0.97; P=.002) in expected anti-Asian hate crimes in the same month, but the relationship was no longer significant after adjusting for the unemployment rate and the emergence of COVID-19 pandemic (P=.11). CONCLUSIONS: A higher negative sentiment level was associated with more hate crimes specifically targeting the Asian community in the same month. The findings highlight the importance of monitoring public sentiment to predict and potentially mitigate hate crimes against Asian individuals.

US Surgeon General Vivek Murthy: To Protect Adolescents, Social Media Needs Warning Labels.

This Medical News article is an interview with US Surgeon General Vivek Murthy, MD, MBA, and JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, about his call for a warning label on social media platforms.

Tapping AI's Strengths-From Operating Room Safety to Wearable Device Interpretation.

In this Medical News interview, Sachin Kheterpal, the University of Michigan Medical School's associate dean for research information technology, joins JAMA Editor in Chief Kirsten Bibbins-Domingo to discuss AI's number-crunching potential for improving patient care.

US Surgeon General Vivek Murthy: Firearm Violence Is a Public Health Crisis.

This Medical News article is an interview with US Surgeon General Vivek Murthy, MD, MBA, and JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, about a new advisory that declares gun violence a public health crisis.

One Day, AI Could Mean Better Mental Health for All.

This Medical News article is an interview with psychiatrist Vikram Patel, chair of the Department of Global Health and Social Medicine at Harvard Medical School.

How Artificial Intelligence Will Enhance Imaging Access and Analysis.

This Medical News article is an interview with Saurabh Jha, a cardiothoracic radiologist and an associate professor of radiology at the University of Pennsylvania, and JAMA Editor in Chief Kirsten Bibbins-Domingo.

Will Generative AI Tools Improve Access to Reliable Health Information?

This Medical News article is an interview with JAMA Editor in Chief Kirsten Bibbins-Domingo and Virologist Davey Smith, head of the Division of Infectious Diseases and Global Public Health at the University of California, San Diego.

Digital Avatars and Personalized Voices-How AI Is Helping to Restore Speech to Patients.

In this Medical News article, Edward Chang, MD, chair of the department of neurological surgery at the University of California, San Francisco Weill Institute for Neurosciences joins JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss the potential for AI to revolutionize communication for those unable to speak due to aphasia.

How Machine Learning Might Help Improve Cardiac Imaging.

In this Medical News interview, University of California, San Francisco, cardiologist Rima Arnaout, joins JAMA Editor in Chief Kirsten Bibbins-Domingo to discuss the transformative potential of AI on cardiac imaging.

Blind Spots, Shortcuts, and Automation Bias-Researchers Are Aiming to Improve AI Clinical Models.

This Medical News article is an interview with University of Michigan computer scientist Jenna Wiens, whose research interests lie at the intersection of AI and health care, and JAMA Editor in Chief Kirsten Bibbins-Domingo.

AI Developers Should Understand the Risks of Deploying Their Clinical Tools, MIT Expert Says.

This Medical News article is an interview with Marzyeh Ghassemi, a machine learning expert at the Massachusetts Institute of Technology who focuses on health care applications, and JAMA Editor in Chief Kirsten Bibbins-Domingo.

How to Navigate the Pitfalls of AI Hype in Health Care.

In this Medical News article, Arvind Narayanan, PhD, a professor of computer science at Princeton University, discusses the benefits of using artificial intelligence in research and clinical settings while remaining cautious of hype, biases, and data privacy issues.

"Scalable Privilege"-How AI Could Turn Data From the Best Medical Systems Into Better Care for All.

This Medical News article is an interview with JAMA Editor in Chief Kirsten Bibbins-Domingo and physician Atul Butte, the University of California Health System's chief data scientist.

How AI Assistants Could Help Answer Patients' Messages-and Potentially Improve Their Outcomes.

This Medical News article is an interview with John Ayers, PhD, MA, vice chief of innovation in the Division of Infectious Diseases & Global Public Health at the University of California, San Diego, the lead author of a recent study on chatbot responses to patient questions.

Vigilance and Protection: How Asian and Pacific Islander, Black, Latina, and Middle Eastern Women Cope with Racism.

BACKGROUND: Research is needed to fully investigate the differential mechanisms racial and ethnic groups use to deal with ongoing intersectional racism in women's lives. The aim of this paper was to understand how Asian American and Pacific Islander, Black, Latina, and Middle Eastern women experience racism-from personal perceptions and interactions to coping mechanisms and methods of protection. METHODS: A purposive sample of 52 participants participated in 11 online racially/ethnically homogeneous focus groups conducted throughout the USA. A team consensus approach was utilized with codebook development and thematic analysis. RESULTS: The findings relate to personal perceptions and interactions related to race and ethnicity, methods of protection against racism, vigilant behavior based on safety concerns, and unity across people of color. A few unique concerns by group included experiences of racism including physical violence among Asian American Pacific Islander groups, police brutality among Black groups, immigration discrimination in Latina groups, and religious discrimination in Middle Eastern groups. Changes in behavior for safety and protection include altering methods of transportation, teaching their children safety measures, and defending their immigration status. They shared strategies to help racial and ethnic minorities against racism including mental health resources and greater political representation. All racial and ethnic groups discussed the need for unity, solidarity, and allyship across various communities of color but for it to be authentic and long-lasting. CONCLUSION: Greater understanding of the types of racism specific groups experience can inform policies and cultural change to reduce those factors.