Clinical validity of the Counseling Center Assessment of Psychological Symptoms-62 (CCAPS-62): Further evaluation and clinical applications.

2012 ◽  
Vol 59 (4) ◽  
pp. 575-590 ◽  
Author(s):  
Andrew A. McAleavey ◽  
Samuel S. Nordberg ◽  
Jeffrey A. Hayes ◽  
Louis G. Castonguay ◽  
Benjamin D. Locke ◽  
...  
Psychotherapy ◽  
2015 ◽  
Vol 52 (4) ◽  
pp. 432-441 ◽  
Author(s):  
Soo Jeong Youn ◽  
Louis G. Castonguay ◽  
Henry Xiao ◽  
Rebecca Janis ◽  
Andrew A. McAleavey ◽  
...  

Assessment ◽  
2021 ◽  
pp. 107319112199876
Author(s):  
Arpita Ghosh ◽  
Christopher R. Niileksela ◽  
Rebecca Janis

The purpose of this study was to examine the factorial invariance of the Counseling Center Assessment of Psychological Symptoms–62 (CCAPS-62) across military background and gender identity. A sample of 2,208 military students and 2,208 nonmilitary students were chosen from a large database of university and college counseling centers. Using exploratory structural equation modeling, findings suggested the CCAPS-62 is mostly invariant across military background and gender identity. Only three item thresholds appeared to be noninvariant across groups. These results suggest comparisons of scores across military background and gender can be made. Latent mean differences across groups were also examined. After controlling for several background variables, there were some differences between males and females on subscales measuring depression, eating concerns, and generalized anxiety, but no differences between military and nonmilitary students. Implications for practice and future research are discussed.


2011 ◽  
Vol 58 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Benjamin D. Locke ◽  
Johanna Soet Buzolitz ◽  
Pui-Wa Lei ◽  
James F. Boswell ◽  
Andrew A. McAleavey ◽  
...  

2020 ◽  
Author(s):  
Guido van Wingen

The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological assessment of brain scans usually does not explain the psychological symptoms. However, that does not mean that psychological symptoms have no neurobiological basis. Hope has therefore been placed on functional MRI, which measures the activity of the brain. However, this has not yet resulted in clinical applications. A multivariate approach using machine learning analysis now appears to be changing this. Machine learning algorithms can already automate various tasks in radiology. Recent studies show that machine learning analysis of MRI images can also provide diagnostic, prognostic and predictive biomarkers for psychiatry. Larger studies are needed to develop clinical applications, such as clinical decision support systems to support personalized treatment choices.


2017 ◽  
Vol 18 (2) ◽  
pp. 21-39
Author(s):  
김은하 ◽  
권민혁 ◽  
장재원 ◽  
최태한

2011 ◽  
Author(s):  
Benjamin D. Locke ◽  
Andrew A. McAleavey ◽  
Yu Zhao ◽  
Pui-Wa Lei ◽  
Jeffrey A. Hayes ◽  
...  

Author(s):  
Paul Ratanasiripong ◽  
Chiachih D. C. Wang ◽  
Nop Ratanasiripong ◽  
Jeffrey A. Hayes ◽  
Orawan Kaewboonchoo ◽  
...  

2019 ◽  
Vol 27 (1) ◽  
pp. 97-105
Author(s):  
Ryo Horita ◽  
Aki Kawamoto ◽  
Akihiro Nishio ◽  
Tadahiro Sado ◽  
Benjamin D. Locke ◽  
...  

1989 ◽  
Vol 36 (1) ◽  
pp. 110-114 ◽  
Author(s):  
Richard W. Johnson ◽  
Robert A. Ellison ◽  
Charles A. Heikkinen

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