Author
Douglas B. Samuel
Other affiliations: Yale University, University of Kentucky, United States Department of Veterans Affairs
Bio: Douglas B. Samuel is an academic researcher from Purdue University. The author has contributed to research in topics: Personality & Personality disorders. The author has an hindex of 38, co-authored 94 publications receiving 6673 citations. Previous affiliations of Douglas B. Samuel include Yale University & University of Kentucky.
Papers published on a yearly basis
Papers
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Stony Brook University1, University of Minnesota2, University of Notre Dame3, University of Vermont4, University of Toronto5, Boston University6, University of Maryland, Baltimore7, Duke University8, University of Kansas9, King's College London10, Columbia University11, Broad Institute12, Purdue University13, University of Iowa14, University of Georgia15, Texas A&M University16, Oklahoma State University–Stillwater17, University of Groningen18, Florida State University19, Uniformed Services University of the Health Sciences20, Bryn Mawr College21, University of North Texas22, University of Otago23, University at Buffalo24, University of Arizona25, University of New South Wales26, Northwestern University27, Emory University28, University of Kentucky29, University of Pittsburgh30, Brown University31
TL;DR: The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies and provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response.
Abstract: The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record
1,635 citations
TL;DR: The empirical FFM profiles generated for each personality disorder were generally congruent at the facet level with hypothesized FFM translations of the DSM-IV-TR personality disorders, but notable exceptions to the hypotheses did occur and even some findings that were consistent with FFM theory could be said to be instrument specific.
870 citations
TL;DR: The authors conclude with a dimensional mental disorder classification that they suggest provides a useful model and suggest that future editions of the Diagnostic and Statistical Manual of Mental Disorders give more recognition to dimensional models of classification.
Abstract: The question of whether mental disorders are discrete clinical conditions or arbitrary distinctions along dimensions of functioning is a long-standing issue, but its importance is escalating with the growing recognition of the frustrations and limitations engendered by the categorical model. The authors provide an overview of some of the dilemmas of the categorical model, followed by a discussion of research that addresses whether mental disorders are accurately or optimally classified categorically or dimensionally. The authors' intention is to document the importance of this issue and to suggest that future editions of the Diagnostic and Statistical Manual of Mental Disorders give more recognition to dimensional models of classification. They conclude with a dimensional mental disorder classification that they suggest provides a useful model.
625 citations
01 Jan 2005
574 citations
University of Minnesota1, Stony Brook University2, University of Notre Dame3, Macquarie University4, University of North Texas5, University at Buffalo6, University of Kentucky7, University of Vermont8, University of Toronto9, University of South Florida10, University of Maryland, Baltimore11, Southern Methodist University12, University of Hawaii13, College of William & Mary14, Ghent University15, University of Utah16, University of Michigan17, Columbia University18, University of Kansas19, Pennsylvania State University20, University of California, Davis21, Georgia State University22, University of Iowa23, University of Georgia24, Texas A&M University25, Oklahoma State University–Stillwater26, University of Groningen27, University of Liverpool28, Florida State University29, Uniformed Services University of the Health Sciences30, Maastricht University31, Bryn Mawr College32, Purdue University33, University of Otago34, University of Maryland, College Park35, University of Arizona36, University of New South Wales37, Northwestern University38, Emory University39, Oak Ridge National Laboratory40, University of Pittsburgh41, Vanderbilt University42
TL;DR: The aims and current foci of the HiTOP Consortium, a group of 70 investigators working together to study empirical classification of psychopathology, are described, which pertain to continued research on the empirical organization of psychopathological constructs; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic.
308 citations
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01 Jan 2016
TL;DR: This is an introduction to the event related potential technique, which can help people facing with some malicious bugs inside their laptop to read a good book with a cup of tea in the afternoon.
Abstract: Thank you for downloading an introduction to the event related potential technique. Maybe you have knowledge that, people have look hundreds times for their favorite readings like this an introduction to the event related potential technique, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their laptop.
2,445 citations
TL;DR: It is found that common mental disorders are strongly linked to personality and have similar trait profiles, and greater attention to these constructs can significantly benefit psychopathology research and clinical practice.
Abstract: We performed a quantitative review of associations between the higher order personality traits in the Big Three and Big Five models (i.e., neuroticism, extraversion, disinhibition, conscientiousness, agreeableness, and openness) and specific depressive, anxiety, and substance use disorders (SUD) in adults. This approach resulted in 66 meta-analyses. The review included 175 studies published from 1980 to 2007, which yielded 851 effect sizes. For a given analysis, the number of studies ranged from three to 63 (total sample size ranged from 1,076 to 75,229). All diagnostic groups were high on neuroticism (mean Cohen's d = 1.65) and low on conscientiousness (mean d = -1.01). Many disorders also showed low extraversion, with the largest effect sizes for dysthymic disorder (d = -1.47) and social phobia (d = -1.31). Disinhibition was linked to only a few conditions, including SUD (d = 0.72). Finally, agreeableness and openness were largely unrelated to the analyzed diagnoses. Two conditions showed particularly distinct profiles: SUD, which was less related to neuroticism but more elevated on disinhibition and disagreeableness, and specific phobia, which displayed weaker links to all traits. Moderator analyses indicated that epidemiologic samples produced smaller effects than patient samples and that Eysenck's inventories showed weaker associations than NEO scales. In sum, we found that common mental disorders are strongly linked to personality and have similar trait profiles. Neuroticism was the strongest correlate across the board, but several other traits showed substantial effects independent of neuroticism. Greater attention to these constructs can significantly benefit psychopathology research and clinical practice.
2,003 citations
01 Jan 2016
TL;DR: The applied missing data analysis is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you for downloading applied missing data analysis. Maybe you have knowledge that, people have look hundreds times for their favorite readings like this applied missing data analysis, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop. applied missing data analysis is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the applied missing data analysis is universally compatible with any devices to read.
1,924 citations
Stony Brook University1, University of Minnesota2, University of Notre Dame3, University of Vermont4, University of Toronto5, Boston University6, University of Maryland, Baltimore7, Duke University8, University of Kansas9, King's College London10, Columbia University11, Broad Institute12, Purdue University13, University of Iowa14, University of Georgia15, Texas A&M University16, Oklahoma State University–Stillwater17, University of Groningen18, Florida State University19, Uniformed Services University of the Health Sciences20, Bryn Mawr College21, University of North Texas22, University of Otago23, University at Buffalo24, University of Arizona25, University of New South Wales26, Northwestern University27, Emory University28, University of Kentucky29, University of Pittsburgh30, Brown University31
TL;DR: The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies and provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response.
Abstract: The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record
1,635 citations