Sherry A. Benton

Recognizing, assessing, and intervening with problems of professional competence.

THIS ARTICLE WAS AUTHORED by members of a workgroup on students with competence problems associated with the Council of Chairs of Training Councils, which is affiliated with the Education Directorate of the American Psychological Association. Nadine J. Kaslow and Nancy J. Rubin took primary responsibility for crafting this manuscript. Nadine J. Kaslow is the workgroup chair. To the extent possible, the remaining authors are listed in the order of their contributions: Nadine J. Kaslow, Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine; Nancy J. Rubin, Department of Psychiatry and Behavioral Medicine, University of Alabama School of Medicine—Tuscaloosa Campus; Linda Forrest, Counseling Psychology and Human Services, University of Oregon; Nancy S. Elman, Psychology in Education, University of Pittsburgh; Barbara A. Van Horne, Department of Psychology, University of Wisconsin-Madison; Sue C. Jacobs, Applied Health and Educational Psychology, Oklahoma State University; Stephen K. Huprich, Department of Psychology, Eastern Michigan University; Sherry A. Benton, Counseling Services, Kansas State University; Victor F. Pantesco, Department of Clinical Psychology, Antioch University New England; Stephen J. Dollinger, Department of Psychology, Southern Illinois University; Catherine L. Grus, Education Directorate, American Psychological Services; Stephen H. Behnke, Ethics Office, American Psychological Association; David S. Shen Miller, Counseling Psychology and Human Services, University of Oregon; Craig N. Shealy, Department of Graduate Psychology, James Madison University; Laurie B. Mintz, Educational, School, and Counseling Psychology, University of Missouri-Columbia; Rebecca Schwartz-Mette, Department of Psychology, University of Missouri-Columbia; Kristi Van Sickle, Department of Psychology, James A. Haley VA Hospital in Tampa; Beverly E. Thorn, Department of Psychology, University of Alabama. CORRESPONDENCE CONCERNING THIS ARTICLE should be addressed to Nadine J. Kaslow, Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, Grady Hospital, 80 Jesse Hill Jr. Drive, Atlanta, GA 30303. E-mail: nkaslow@emory.edu Professional Psychology: Research and Practice Copyright 2007 by the American Psychological Association 2007, Vol. 38, No. 5, 479–492 0735-7028/07/

Evidence-Based Practice in Psychology among College Counseling Center Clinicians

12.00 DOI: 10.1037/0735-7028.38.5.479

Self-Reflective Sentiment Analysis.

ABSTRACT This empirically based study sought to discover factors underlying diverse sources of information used to inform therapy practice, perceived salience of sources of evidence for clinical practice, importance of common factors to therapy efficiency, and beliefs about evidence-based practice, particularly in the form of evidence-supported treatments (ESTs). The investigation also sought to uncover possible relationships between a variety of individually based demographics and institutionally based demographics and their possible relationship to factors underlying these diverse sources of information. A number of interesting findings emerged.

College student protective strategies and drinking consequences.

As self-directed online anxiety treatment and e-mental health programs become more prevalent and begin to rapidly scale to a large number of users, the need to develop automated techniques for monitoring patient progress and detecting early warning signs is at an alltime high. While current online therapy systems work based on explicit quantitative feedback from various survey measures, little attention has been paid thus far to the large amount of unstructured free text present in the monitoring logs and journals submitted by patients as part of the treatment process. In this paper, we automatically categorize patients’ internal sentiment and emotions using machine learning classifiers based on n-grams, syntactic patterns, sentiment lexicon features, and distributed word embeddings. We report classification metrics on a novel mental health dataset.