Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity
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 metho...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1463116/full |
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| author | Ethel Siew Ee Tan Hong Ming Tan Kah Vui Fong Sheryl Yu Xuan Tey Nikita Rane Chong Wei Ho Zhao Yuan Tan Rachel Jing Min Ong Chloe Teo Jerall Yu Maxine Lee An Rae Teo Sin Kee Ong Xin Ying Lim Jin Lin Kee Jussi Keppo Geoffrey Chern-Yee Tan |
| author_facet | Ethel Siew Ee Tan Hong Ming Tan Kah Vui Fong Sheryl Yu Xuan Tey Nikita Rane Chong Wei Ho Zhao Yuan Tan Rachel Jing Min Ong Chloe Teo Jerall Yu Maxine Lee An Rae Teo Sin Kee Ong Xin Ying Lim Jin Lin Kee Jussi Keppo Geoffrey Chern-Yee Tan |
| author_sort | Ethel Siew Ee Tan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6fd380b2267a458ab9fda34cc0f6a5d7 |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-6fd380b2267a458ab9fda34cc0f6a5d72025-02-10T06:48:34ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-02-011510.3389/fpsyt.2024.14631161463116Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severityEthel Siew Ee Tan0Hong Ming Tan1Kah Vui Fong2Sheryl Yu Xuan Tey3Nikita Rane4Chong Wei Ho5Zhao Yuan Tan6Rachel Jing Min Ong7Chloe Teo8Jerall Yu9Maxine Lee10An Rae Teo11Sin Kee Ong12Xin Ying Lim13Jin Lin Kee14Jussi Keppo15Geoffrey Chern-Yee Tan16Department of Mood and Anxiety, Institute of Mental Health, Singapore, SingaporeNational University of Singapore (NUS) Business School, National University of Singapore, Singapore, SingaporeCollege of Humanities and Sciences, National University of Singapore, Singapore, SingaporeDepartment of Mood and Anxiety, Institute of Mental Health, Singapore, SingaporeDepartment of Mood and Anxiety, Institute of Mental Health, Singapore, SingaporeCollege of Humanities and Sciences, National University of Singapore, Singapore, SingaporeLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, SingaporeFaculty of Arts and Social Sciences, National University of Singapore, Singapore, SingaporeLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, SingaporeCollege of Humanities and Sciences, National University of Singapore, Singapore, SingaporeCollege of Humanities and Sciences, National University of Singapore, Singapore, SingaporeCollege of Humanities and Sciences, National University of Singapore, Singapore, SingaporeClinical Research Unit, National Healthcare Group (NHG) Polyclinics, Singapore, SingaporeFaculty of Arts and Social Sciences, National University of Singapore, Singapore, SingaporeMinistry of Education, Singapore, SingaporeInstitute of Operations Research and Analytics, National University of Singapore, Singapore, SingaporeDepartment of Mood and Anxiety, Institute of Mental Health, Singapore, SingaporeIntroductionThe 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1463116/fullself-schemaself-conceptself-referential processingpersonalitydepression |
| spellingShingle | Ethel Siew Ee Tan Hong Ming Tan Kah Vui Fong Sheryl Yu Xuan Tey Nikita Rane Chong Wei Ho Zhao Yuan Tan Rachel Jing Min Ong Chloe Teo Jerall Yu Maxine Lee An Rae Teo Sin Kee Ong Xin Ying Lim Jin Lin Kee Jussi Keppo Geoffrey Chern-Yee Tan Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity Frontiers in Psychiatry self-schema self-concept self-referential processing personality depression |
| title | Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity |
| title_full | Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity |
| title_fullStr | Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity |
| title_full_unstemmed | Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity |
| title_short | Evaluating the relative predictive validity of measures of self-referential processing for depressive symptom severity |
| title_sort | evaluating the relative predictive validity of measures of self referential processing for depressive symptom severity |
| topic | self-schema self-concept self-referential processing personality depression |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1463116/full |
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