Towards a Novel Model of Mental Health Sessions
Towards a Novel Model of Mental Health Symposia Session
There is a general agreement that we need to move beyond traditional deficit-and illness-based models of mental health. We will explore how advances in basic knowledge, the translation of laboratory findings, and innovations in models of care are reshaping even the very concept of mental health. By integrating data-driven research, digital interventions, and insights into individual differences, we aim to advance a model that emphasizes proactive well-being, resilience, and optimal functioning.
The discussion will highlight how an integrative and forward-looking approach—one that synthesizes psychological science, digital innovation, and personalized interventions—can contribute to a fundamental transformation of mental health systems and offer a comprehensive perspective on how mental health can be reconceptualized in the face of current and future demands.
Speakers:
- Claudi L.H. Bockting, Amsterdam University Medical Centers, The Netherlands
- Heleen M.M. Riper, Vrije Universiteit Amsterdam, The Netherlands
- Eiko I. Fried, Leiden University, The Netherlands
ISS Linked Flash Talk Session
11:15 – 12:15 (11:15 AM – 12:15 PM)
Political Is Personal: Investigating Election-Related Stress Coping during the 2024 US Presidential Election
Presenting Author: Yun Evelina Bao, New York University, USA
We conducted a 5-wave study (N=1188) that tracked emotions, well-being, emotion regulation (ER), and political actions during the 2024 U.S. election. Successful ER predicted less negative emotions, better well-being, but lower political action intentions. However, the associations between ER and psychological benefits differed across racial groups.
Structured AI Dialogues Can Increase Happiness and Meaning In Life
Presenting Author: Jonas Schoene, Stanford University, USA
We preregistered a large trial (N=2,936) testing four structured AI chatbots (savoring, gratitude, meaning, hero’s journey). Versus a neutral control, all improved affect, meaning, life satisfaction, anxiety, and depressed mood; effects generalized to high-symptom subgroups and increased therapy interest. A survey (N=3,056) showed broad public willingness to use validated chatbots.
A Systematic Comparison of Traditional and Machine Learning Algorithms for Predicting Psychiatric Outcomes: Prediction of Response to Antidepressant Treatments In Major Depression
Presenting Author: Colin Xu, University of Idaho, USA
Machine learning for predicting outcomes has been gaining recognition in psychiatry. We conducted a systematic comparison of machine learning algorithms using data from a clinical trial of 452 patients. Machine learning models outperformed traditional model building approaches for predicting treatment response.
Distinct Pathways from Threat and Deprivation to Delinquency From Early Childhood to Adolescence: A Person-Centered Test of the Dimensional Model of Adversity and Psychopathology In the Millennium Cohort Study
Presenting Author: Yuhui Liu, University of Edinburgh, United Kingdom
This exploratory study tested DMAP using longitudinal data from 9,980 UK youth. Latent adversity classes from birth to age 11 predicted delinquency at 17 through emotional problems and executive functioning. Findings partially supported DMAP and identified targets for interventions aimed at strengthening cognition and emotion regulation to reduce delinquency.
Differences In Adolescents’ Past-Year Suicide Attempts and Ideation Across Racial, Gender, and Intersecting Identities
Presenting Author: Nadia Elizabeth Rodriguez, University of Central Florida, USA
Suicide risk among ~83,000 gender and racial diverse adolescents was analyzed. Risk among gender-diverse youth was considerably high. Controlling for sexual orientation resulted in a decrease in risk across all racial identities, suggesting this aspect of identity may be particularly relevant to risk. Broadly, racially diverse youth endorsed elevated risk.
