کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10304409 | 545712 | 2013 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Determining severity subtypes of depression with a self-report questionnaire
ترجمه فارسی عنوان
تعیین شدت زیر نوع افسردگی با پرسشنامه خود گزارش دهی
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کلمات کلیدی
افسردگی، شدت مقیاس پیامدهای بالینی افسردگی مفید است. خود گزارش،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
روانپزشکی بیولوژیکی
چکیده انگلیسی
The American Psychiatric Association's recently revised guidelines for the treatment of major depressive disorder indicated that it is important to consider symptom severity in initial treatment selection. In the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project, we conducted two studies of psychiatric outpatients examining the correlates of severity classification based on a self-report depression scale. The first sample consisted of 470 depressed outpatients who completed the Clinically Useful Depression Outcome Scale (CUDOS) and measures of psychosocial morbidity at the time of presentation. The second sample consisted of 112 depressed outpatients who completed the CUDOS and were evaluated with the Hamilton Depression Rating Scale at baseline and after 3 months of treatment. Compared to mildly depressed patients, moderately depressed patients reported significantly more psychosocial morbidity across all functional domains. The same differences were found between moderately and severely depressed patients. Greater severity of depression was associated with lower rates of response and remission. The results of the present studies suggest that a self-report depression questionnaire can validly subtype depressed patients according to gradations of severity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Psychiatry Research - Volume 206, Issue 1, 30 March 2013, Pages 98-102
Journal: Psychiatry Research - Volume 206, Issue 1, 30 March 2013, Pages 98-102
نویسندگان
Mark Zimmerman, Jennifer H. Martinez, Michael Friedman, Daniela A. Boerescu, Naureen Attiullah, Cristina Toba,