Large language models for the mental health community: framework for translating code to care.

Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation.

Leveraging Social Media to Predict COVID-19-Induced Disruptions to Mental Well-Being Among University Students: Modeling Study.

Background: Large-scale crisis events such as COVID-19 often have secondary impacts on individuals' mental well-being. University students are particularly vulnerable to such impacts. Traditional survey-based methods to identify those in need of support do not scale over large populations and they do not provide timely insights.

Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.

Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm.

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach.

Background: Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use.

Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study.

Background: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed.

Objective: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD.

Misogynistic Extremism: A Scoping Review.

In recent years, the concept of "misogynistic extremism" has emerged as a subject of interest among scholars, governments, law enforcement personnel, and the media. Yet a consistent understanding of how misogynistic extremism is defined and conceptualized has not yet emerged. Varying epistemological orientations may contribute to the current conceptual muddle of this topic, reflecting long-standing and on-going challenges with the conceptualization of its individual components.

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses.

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as (WiMob).

Using Social Media to Examine Themes Surrounding Fentanyl Misuse and Risk Indicators.

Opioid misuse is a crisis in the United States, and synthetic opioids such as fentanyl pose risks for overdose and mortality. Individuals who misuse substances commonly seek information and support online due to stigma and legal concerns, and this online networking may provide insight for substance misuse prevention and treatment. We aimed to characterize topics in substance-misuse related discourse among members of an online fentanyl community.

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data.

Background: In the United States, 1 out of every 3 people lives in a mental health professional shortage area. Shortage areas tend to be rural, have higher levels of poverty, and have poor mental health outcomes. Previous work has demonstrated that these poor outcomes may arise from interactions between a lack of resources and lack of recognition of mental illness by medical professionals.

Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study.

Background: Previous research has shown the feasibility of using machine learning models trained on social media data from a single platform (eg, Facebook or Twitter) to distinguish individuals either with a diagnosis of mental illness or experiencing an adverse outcome from healthy controls. However, the performance of such models on data from novel social media platforms unseen in the training data (eg, Instagram and TikTok) has not been investigated in previous literature.

Objective: Our study examined the feasibility of building machine learning classifiers that can effectively predict an upcoming psychiatric hospitalization given social media data from platforms unseen in the classifiers' training data despite the preliminary evidence on identity fragmentation on the investigated social media platforms.

From promise to practice: towards the realisation of AI-informed mental health care.

In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, and regulatory perspectives. The real-world implementation of these tools is increasingly considered the prime solution for key issues in mental health, such as delayed, inaccurate, and inefficient care delivery. Similarly, machine-learning-based empirical strategies are becoming commonplace in psychiatric research because of their potential to adequately deconstruct the biopsychosocial complexity of mental health disorders, and hence to improve nosology of prognostic and preventive paradigms.

The promise of a model-based psychiatry: building computational models of mental ill health.

Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions.

Interventions to mitigate vaping misinformation: protocol for a scoping review.

Background: The impact of misinformation about vapes' relative harms compared with smoking may lead to increased tobacco-related burden of disease. To date, no systematic efforts have been made to chart interventions that mitigate vaping-related misinformation. We plan to conduct a scoping review that seeks to fill gaps in the current knowledge of interventions that mitigate vaping-related misinformation.

Social media-based interventions for adolescent and young adult mental health: A scoping review.

Background: Mental health conditions are common among adolescents and young adults, yet few receive adequate mental health treatment. Many young people seek support and information online through social media, and report preferences for digital interventions. Thus, digital interventions deployed through social media have promise to reach a population not yet engaged in treatment, and at risk of worsening symptoms.

Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory.

Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay, transgender) people experience minority stress (i.

The Reintegration Journey Following A Psychiatric Hospitalization: Examining the Role of Social Technologies.

For people diagnosed with mental health conditions, psychiatric hospitalization is a major life transition, involving clinical treatment, crisis stabilization and loss of access of social networks and technology. The period after hospitalization involves not only management of the condition and clinical recovery but also re-establishing social connections and getting back to social and vocational roles for successful reintegration - a significant portion of which is mediated by social technology. However, little is known about how people get back to social lives after psychiatric hospitalization and the role social technology plays during the reintegration process.

Interventions to mitigate COVID-19 misinformation: protocol for a scoping review.

Background: The duration and impact of the COVID-19 pandemic depends in a large part on individual and societal actions which is influenced by the quality and salience of the information to which they are exposed. Unfortunately, COVID-19 misinformation has proliferated. To date, no systematic efforts have been made to evaluate interventions that mitigate COVID-19-related misinformation.

A Social Media Study on Demographic Differences in Perceived Job Satisfaction.

Effective ways to measure employee job satisfaction are fraught with problems of scale, misrepresentation, and timeliness. Current methodologies are limited in capturing subjective differences in expectations, needs, and values at work, and they do not lay emphasis on demographic differences, which may impact people's perceptions of job satisfaction. This study proposes an approach to assess job satisfaction by leveraging large-scale social media data.

A Review on Strategies for Data Collection, Reflection, and Communication in Eating Disorder Apps.

Eating disorders (EDs) constitute a mental illness with the highest mortality. Today, mobile health apps provide promising means to ED patients for managing their condition. Apps enable users to monitor their eating habits, thoughts, and feelings, and offer analytic insights for behavior change.