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Published on: July 24, 2016
The challenges of forecasting resilience
Luke J Chang1, Marianne Reddan1, Yoni K Ashar1
1Department of Psychology & Neuroscience,University of Colorado,Boulder,CO 80309.luke.chang@colorado.edumarianne.reddan@colorado.edujonathan.ashar@colorado.eduhedwig.eisenbarth@colorado.edutor.wager@colorado.eduhttp://cosanlab.comhttp://wagerlab.colorado.edu.
Developing predictive resilience models requires extensive data and advanced analytical techniques. This approach utilizes a translational and transdiagnostic framework for a comprehensive understanding of mental health.
Area of Science:
- Psychology
- Psychiatry
- Data Science
Background:
- The translational and transdiagnostic framework offers a novel approach to understanding mental health.
- Developing prospective models of resilience is crucial for mental health research.
Purpose of the Study:
- To explore the challenges and requirements for developing prospective models of resilience.
- To apply a translational and transdiagnostic framework to resilience modeling.
Main Methods:
- Utilizing a translational and transdiagnostic framework.
- Requires large-scale datasets with dense intraindividual temporal sampling.
- Employing innovative analytical methods.
Main Results:
- Developing prospective resilience models is a challenging endeavor.
- Success is contingent upon substantial data and advanced analytics.
Conclusions:
- The proposed framework necessitates significant advancements in data collection and analysis.
- Future research should focus on acquiring large, temporally dense datasets and innovative methods.
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