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Contribution title Development of a Computer-Assisted Assessment Tool (CAAT) for Monitoring Adolescent Mental Health Outcomes in Latvia.
Contribution code D2.056
Authors
  1. Nikita Bezborodovs Riga Stradins University Presenter
  2. Zane Gulbe Riga Stradins University
  3. Linda Nauzere Riga Stradins University
  4. Jelena Kolesnikova Riga Stradins University
  5. Viktorija Perepjolkina Riga Stradins University
  6. Ilona Krone Riga Stradins University
  7. Ainars Stepens Riga Stradins University
Form of presentation Poster
Topic
  • T07 - Assessment / testing
Abstract Aims: In Lativa, similar to other small national states, there is a severe lack of validated mental health screening and assessment tools. This project aims to develop a Computer-Assisted Assessment Tool (CAAT) to measure and monitor mental health outcomes in Latvian adolescents aged 13–19, with a focus on identifying risk factors for mental health difficulties, including suicidal behaviour, in the post-COVID pandemic context.
Methods: The CAAT integrates traditional self-report measures with a measurement of psychophysiological parameters, such as reaction time and response pressure, for a comprehensive assessment of risks. CAAT self-report scales are designed to cover both clinical mental health diagnoses, with item pools based on ICD-11 criteria (19 scales) and personality traits (23 scales). The development process involved collaboration between Riga Stradiņš University (development of the contents of the screening instrument), Riga Technical University (development of the test administration device), and pediatric mental health experts, with iterative evaluations for content and face validity by expert panels and adolescent participants (n=200). The psychometric properties of CAAT are going to be tested in clinical and general population samples to refine the tool and ensure reliability and standardisation.
Results and Conclusions: In the piloting phase, CAAT demonstrated strong psychometric properties, including high internal consistency (Cronbach’s alpha >0.7) and robust factor structures. Its multidimensional approach allows for the efficient identification of mental health difficulties while capturing dynamic psychophysiological indicators of mental state. Future research will focus on validating the CAAT’s predictive accuracy and enhancing its implementation to improve adolescent mental health care access and outcomes, particularly in addressing suicide risk post-pandemic.