A5103104671- Suicide and Self-Harm Studies
- Healthcare Policy and Management
- Mental Health via Writing
- Digital Mental Health Interventions
- Mental Health Treatment and Access
- Patient Satisfaction in Healthcare
- Health Systems, Economic Evaluations, Quality of Life
- Global Health Care Issues
- Gender, Feminism, and Media
- Grief, Bereavement, and Mental Health
- Healthcare cost, quality, practices
- Law, Economics, and Judicial Systems
- Media Influence and Health
- Impact of Technology on Adolescents
- Insurance and Financial Risk Management
- Palliative Care and End-of-Life Issues
- Traumatic Brain Injury Research
- Language Development and Disorders
- Medical Malpractice and Liability Issues
- Merger and Competition Analysis
- Pharmaceutical industry and healthcare
- Reading and Literacy Development
- Child and Animal Learning Development
- Occupational and Professional Licensing Regulation
- Mental Health Research Topics
Massachusetts Institute of Technology
2023-2025
Harvard University
2021-2024
Harvard University Press
2021-2024
Stanford University
1999-2008
National Bureau of Economic Research
1999-2005
Northwestern University
2002
Council of Economic Advisers
2002
University of Chicago
1996-2002
University of Illinois Chicago
2002
Federal Trade Commission
1999
In their 1984 article, Priest and Klein show that a simple divergent expectations model of the decision to litigate leads plaintiff success rate at trial approaches 50 percent as fraction cases going zero. However, an extensive empirical literature has documented plaintiffs win far fewer than half cases. As observe, this conflict between predictions may be attributable violations in data assumptions behind model. Based on from 3,529 cases, we find "multimodal" case characteristics associated...
Abstract The Technological Change in Health Care Research Network collected unique patient‐level data on three procedures for treatment of heart attack patients (catheterization, coronary artery bypass grafts and percutaneous transluminal angioplasty) 17 countries over a 15‐year period to examine the impact economic institutional factors technology adoption. Specific are shown be important uptake these technologies. Health‐care systems characterized as public contract reimbursement have...
Interest in developing machine learning models that use electronic health record data to predict patients' risk of suicidal behavior has recently proliferated. However, whether and how such might be implemented useful clinical practice remain unknown. To ultimately make automated suicide risk-prediction practice, thus better prevent patient suicides, it is critical partner with key stakeholders, including the frontline providers who will using tools, at each stage implementation process.The...
Suicide is a major public health concern in the United States, but few effective and scalable interventions exist to help those with suicidal thoughts. We hypothesized that reading first-person narratives about working through thoughts would reduce desire die among adults this effect be mediated by increased perceived shared experience optimism.Using randomized waitlist-controlled trial, we tested of digital narrative-based bibliotherapy 528 visiting social media platform dedicated providing...
Background: Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding experience of suicidal thoughts. We developed an algorithm identify on a military-specific social media platform. Methods: Publicly-shared posts (n = 8449) from platform were reviewed labeled by our team the presence/absence thoughts behaviors used train several machine learning models such posts. Results: The best performing model was deep...
Every day, individuals post suicide notes on social media asking for support, resources, and reasons to live. Some posts receive few comments while others many. While prior studies have analyzed whether specific responses are more or less helpful, it is not clear if the quantity of received beneficial in reducing symptoms keeping user engaged with platform hence life. In present study, we create a large dataset users' first r/SuicideWatch (SW) from Reddit (N=21,274), collect as well user's...
Abstract Background Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding experience of suicidal thoughts. We developed an algorithm identify posts containing suicide-related content on a military-specific social media platform. Methods Publicly-shared ( n = 8449) from platform were reviewed labeled by our team the presence/absence thoughts behaviors used train several machine learning models such posts....
Background: Interest in developing machine learning algorithms that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. Whether and how such models might be implemented useful clinical practice, however, remains unknown. In order ultimately make automated suicide prediction thus better prevent patient suicides, it is critical partner with key stakeholders (including the frontline providers who will using tools) at each stage...
Narratives about invisible disabilities are poorly represented in public discourse and often go undisclosed [1], leading to false assumptions, discrimination, stigma [2] against those who experience these conditions. To address issues, recent studies have suggested that disclosure of first-person narratives should be increased [3]. understand the mechanisms affecting recipients such narratives, present study evaluates how social media users (N = 124) engage affectively with this content a...
<sec> <title>BACKGROUND</title> Interest in developing machine learning models that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. However, whether and how such might be implemented useful clinical practice remain unknown. To ultimately make automated suicide risk–prediction practice, thus better prevent patient suicides, it is critical partner with key stakeholders, including the frontline providers who will using tools, at each...