Speaker
Description
Introduction: Network analysis (NA) is increasingly being used in clinical psychology to represent psychopathological processes as complex systems of interacting symptoms. Fully Idiographic Network Analysis (FINA) estimates individual level symptom networks through intensive data collection. This study evaluated FINA’s clinical validity and utility in 15 clinician-patient dyads.
Methods: To assess clinical validity, a training-test approach was used to compare clinical models (i.e., symptom networks predicted by clinicians for their patients), empirical models (i.e., symptom networks estimated from patient data using FINA), and null models (assuming symptom constancy) in predicting patient functioning. To assess clinical utility, self-report questionnaires were administered to both clinicians and patients, focusing on FINA’s applicability across different patients and settings, as well as its acceptability, ease of use and understanding, and cost.
Results: Results from mixed-effects models indicated that FINA outperformed both clinical and null models in predicting patient functioning, with improved predictive accuracy as the data volume increased. Both clinicians and patients expressed a generally positive attitude toward the utility of FINA, with clinicians rating it more favorably in terms of global clinical utility, applicability across different patients and settings, ease of use and understanding, and perceived costs. Both clinicians and patients found FINA equally acceptable, as a tool that complements and supports clinical work.
Conclusions: These findings underscore the validity of FINA in enhancing clinical assessments and its perceived utility in clinical setting. The study suggests important research and clinical implications, offering pathways to expand FINA’s use in clinical practice and further improve its effectiveness.
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