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ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Psychopathology
Volume 15 - 2024 |
doi: 10.3389/fpsyt.2024.1463116
This article is part of the Research Topic The Way We See Ourselves and Others as a Central Issue in Mental Health: The Current Evidence on Self-esteem and Self-Schemas View all 4 articles
Evaluating the Relative Predictive Validity of Measures of Self-Referential Processing for Depressive Symptom Severity
Provisionally accepted
Ethel Siew Ee Tan
1*
Hong Ming Tan
2
Kah Vui Fong
3*
Chong Wei Ho
3*
Chloe Teo
4*
Zhao Yuan Tan
4*
Rachel Ong
5
Jerall Yu
3
Maxine Lee
3*
An Rae Teo
3*
Sin Kee Ong
6
Xin Ying Lim
5*
Jin Lin Kee
7*
Nikita Rane
1*
Sheryl Yu Xuan Tey
1
Jussi Keppo
2*
Geoffrey Chern-Yee Tan
1*- 1 Institute of Mental Health, Singapore, Singapore
- 2 Institute of Operations Research and Analytics, National University of Singapore, Singapore, Singapore
- 3 College of Humanities and Science, National University of Singapore, Singapore, Singapore
- 4 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- 5 Faculty of Arts and Social Sciences, National University of Singapore, Singapore, Singapore
- 6 NHG Polyclinics, Singapore, Singapore
- 7 Ministry of Education (Singapore), Singapore, Singapore
The 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. This 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. Results 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. These 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.
Keywords: self-schema1, self-concept2, self-referential processing3, personality4, Depression5
Received: 11 Jul 2024; Accepted: 11 Dec 2024.
Copyright: © 2024 Tan, Tan, Fong, Ho, Teo, Tan, Ong, Yu, Lee, Teo, Ong, Lim, Kee, Rane, Tey, Keppo and Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ethel Siew Ee Tan, Institute of Mental Health, Singapore, Singapore
Kah Vui Fong, College of Humanities and Science, National University of Singapore, Singapore, Singapore
Chong Wei Ho, College of Humanities and Science, National University of Singapore, Singapore, Singapore
Chloe Teo, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 639798, Singapore
Zhao Yuan Tan, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 639798, Singapore
Maxine Lee, College of Humanities and Science, National University of Singapore, Singapore, Singapore
An Rae Teo, College of Humanities and Science, National University of Singapore, Singapore, Singapore
Xin Ying Lim, Faculty of Arts and Social Sciences, National University of Singapore, Singapore, 117570, Singapore
Jin Lin Kee, Ministry of Education (Singapore), Singapore, 138675, Singapore
Nikita Rane, Institute of Mental Health, Singapore, Singapore
Jussi Keppo, Institute of Operations Research and Analytics, National University of Singapore, Singapore, Singapore
Geoffrey Chern-Yee Tan, Institute of Mental Health, Singapore, Singapore
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