Machine learning model (ELSA) for the identification and prediction of psychological stress of students: A validation study

The primary aim is to validate the detection accuracy of the ELSA model across diverse countries and cohorts.

The secondary aim is to examine associations and predictive relationships between substance use, traumatic experiences, suicidality, psychotic symptoms, psychological resilience, and non-psychiatric variables assessed by ELSA, including comparisons between high-income and low- and middle-income countries.

The tertiary aim is to utilise cross-national university student data to train models that enhance the generalisability and cultural applicability of ELSA.

In collaboration with: Gefyra AI Technologies

Verantwortliche Personen
Leitung: Kristina Adorjan
Team: Juliane Kahl