The most effective and useful way to diagnose and subtype depression has been a long debated topic which even now does not have a definite answer. The biopsychosocial approach to diagnosis may be a solution to this problem by linking various etiologies to symptom presentation. The biopsychosocial model, in regard to depression, takes into account biological risk factors/contributors, psychological or cognitive risk factors/contributors, and social risk factors/contributors to depression when making diagnosis and subtyping determinations. However, the most effective way to use this model in the assessment, diagnosis, and treatment of depression is not yet clear. In this study, the utility of the biopsychosocial model as an effective approach to conceptualizing and treating depression was assessed by testing hypotheses that showed that etiological contributors are related to the presence and differential presentation of depression, and that these etiologically-based subtypes of depression respond differently to different forms of treatment. These hypotheses were tested using data from the National Comorbidity Survey - Replication (NCS-R). Results showed that the biopsychosocial model can effectively predict the presence, severity and chronicity of depression, and may inform specific biopsychosocially-based subtypes. No conclusions could be drawn regarding success in treatment based on the biopsychosocial model. Future directions for research based on the current study are discussed.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc68013 |
| Date | 05 1900 |
| Creators | McGill, Brittney C. |
| Contributors | Jenkins, Sharon Rae, Guarnaccia, Charles A., Callahan, Jennifer |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Copyright, McGill, Brittney C., Copyright is held by the author, unless otherwise noted. All rights reserved. |
Page generated in 0.0026 seconds