The Early Psychosis Screener (EPS): Item development and qualitative validation

• Type of Material: Article
• Authors: Benjamin B. Brodey, TeleSage, Inc.; Jean Addington, University of Calgary; Michael B. First, Columbia University Medical Center; Diana O. Perkins, University of North Carolina at Chapel Hill; Scott W. Woods, Connecticut Mental Health Center; Elaine Walker, Emory University; Barbara Walsh, Connecticut Mental Health Center; Jennifer M. Nieri, University of North Carolina at Chapel Hill; M. Brad Nunn, Centerstone Tennessee; John Putz, Centerstone Research Institute; Inger S. Brodey, TeleSage, Inc.
• Language: English
• Date: 2018-07-01
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 Elsevier B.V.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.schres.2017.11.027
• Title of Journal or Parent Work: Schizophrenia Research
• ISSN: 0920-9964
• Volume: 197
• Start Page: 504
• End Page: 508
• Grant/Funding Information: This study was supported by a National Institutes of Health (NIH) grant #MH094023.
• Keywords: PQ-B; CAARMS; schizophrenia; SIPS; NAPLS; prodromal; psychosis; screener
• Research Categories: Psychology, Clinical

Abstract

A panel of experts assembled and analyzed a comprehensive item bank from which a highly sensitive and specific early psychosis screener could be developed. Twenty well-established assessments relating to the prodromal stage, early psychosis, and psychosis were identified. Using DSM-5 criteria, we identified the core concepts represented by each of the items in each of the assessments. These granular core concepts were converted into a uniform set of 490 self-report items using a Likert scale and a ‘past 30 days’ time frame. Partial redundancy was allowed to assure adequate concept coverage. A panel of experts and TeleSage staff rated these items and eliminated 189 items, resulting in 301 items. The items were subjected to five rounds of cognitive interviewing with 16 individuals at clinically high risk for psychosis and 26 community mental health center patients. After each round, the expert panel iteratively reviewed, rated, revised, added, or deleted items to maximize clarity and centrality to the concept. As a result of the interviews, 36 items were revised, 52 items were added, and 205 items were deleted. By the last round of cognitive interviewing, all of the items were clearly understood by all participants. In future work, responses to the final set of 148 items and machine learning techniques will be used to quantitatively identify the subset of items that will best predict clinical high-risk status and conversion.