AUTHOR=Tan Ethel Siew Ee , Tan Hong Ming , Fong Kah Vui , Tey Sheryl Yu Xuan , Rane Nikita , Ho Chong Wei , Tan Zhao Yuan , Ong Rachel Jing Min , Teo Chloe , Yu Jerall , Lee Maxine , Teo An Rae , Ong Sin Kee , Lim Xin Ying , Kee Jin Lin , Keppo Jussi , Tan Geoffrey Chern-Yee 

TITLE=Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 15 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1463116

DOI=10.3389/fpsyt.2024.1463116

ISSN=1664-0640

ABSTRACT=IntroductionThe self-referential encoding task (SRET) has a number of implicit measures which are associated with various facets of depression, including depressive symptoms. While some measures have proven robust in predicting depressive symptoms, their effectiveness can vary depending on the methodology used. Hence, understanding the relative contributions of population differences, word lists and calculation methods to these associations with depression, is crucial for translating the SRET into a clinical screening tool. MethodsThis study systematically investigated the predictive accuracy of various SRET measures across different samples, including one clinical population matched with healthy controls and two university student populations, exposed to differing word lists. Participants completed the standard SRET and its variations, including Likert scales and matrix formats. Both standard and novel SRET measures were calculated and compared for their relative and incremental contribution to their associations with depression, with mean squared error (MSE) used as the primary metric for measuring predictive accuracy. ResultsResults showed that most SRET measures significantly predicted depressive symptoms in clinical populations but not in healthy populations. Notably, models with task modifications, such as Matrix Endorsement Bias and Likert Endorsement Sum Bias, achieved the lowest mean squared error (MSE), indicating better predictive accuracy compared to standard Endorsement Bias measures. DiscussionThese findings imply that task modifications such as utilising Likert-response options and the use of longer word lists may enhance the effectiveness of screening methods in both clinical and research settings, potentially improving early detection and intervention for depression.