Nev Jones1,2, Julia Godzikovskaya3, Zhen Zhao3, Anthony Vasquez3, Al Gilbert3, Larry Davidson2. 1. Department of Mental Health Law and Policy, Louis de la Parte Mental Health Institute, University of South Florida, Tampa, Florida. 2. Department of Psychiatry, Program for Recovery and Community Health, Yale University, New Haven, Connecticut. 3. Division for Client Centered Outcomes Research in Public Mental Health, Felton Institute, San Francisco, California.
Abstract
AIM: In order to strengthen specialized early intervention in psychosis (EIP) services, a contextually nuanced understanding of psychosocial forces driving suboptimal treatment response is critical. This study sought to examine factors driving poor outcomes through a systematic emic coding of psychosocial assessments for discharged EIP clients categorized as leaving with all treatment goals met (AG) or no treatment goals met (NG). METHODS: Psychosocial assessments at baseline, 1 year, 2 years and discharge were extracted from an EIP research registry and systematically coded. One hundred and thirty clients met the study's inclusion criteria (72 NG, 58 AG) from a larger pool of 278. Assessments were coded, quantized and analysed using a combination of basic inferential statistics and thematic analysis. RESULTS: Structural adversity, individual trauma, history of aggression/violence, limited insight and long treatment histories prior to EIP, were strong and significant predictors of poor client outcomes (NG), while motivation for treatment, college goals or preparedness at baseline, baseline engagement in constructive activities, social strengths, individual strengths, talents and family support strongly predicted better outcomes (AG). Race/ethnicity also significantly predicted outcome group. Analyses underscore the powerful impact of multiple converging forms of structural disadvantage, on the one hand, and individual, family and social strengths and supports on the other, in shaping clients' response to EIP treatment. CONCLUSIONS: Findings emphasize the importance of greater empirical attention to background structural and socio-economic conditions among early psychosis clients and their multifaceted impacts and underscore the potential value of programmatic components explicitly designed to support clients from multiply disadvantaged backgrounds.
AIM: In order to strengthen specialized early intervention in psychosis (EIP) services, a contextually nuanced understanding of psychosocial forces driving suboptimal treatment response is critical. This study sought to examine factors driving poor outcomes through a systematic emic coding of psychosocial assessments for discharged EIP clients categorized as leaving with all treatment goals met (AG) or no treatment goals met (NG). METHODS:Psychosocial assessments at baseline, 1 year, 2 years and discharge were extracted from an EIP research registry and systematically coded. One hundred and thirty clients met the study's inclusion criteria (72 NG, 58 AG) from a larger pool of 278. Assessments were coded, quantized and analysed using a combination of basic inferential statistics and thematic analysis. RESULTS: Structural adversity, individual trauma, history of aggression/violence, limited insight and long treatment histories prior to EIP, were strong and significant predictors of poor client outcomes (NG), while motivation for treatment, college goals or preparedness at baseline, baseline engagement in constructive activities, social strengths, individual strengths, talents and family support strongly predicted better outcomes (AG). Race/ethnicity also significantly predicted outcome group. Analyses underscore the powerful impact of multiple converging forms of structural disadvantage, on the one hand, and individual, family and social strengths and supports on the other, in shaping clients' response to EIP treatment. CONCLUSIONS: Findings emphasize the importance of greater empirical attention to background structural and socio-economic conditions among early psychosis clients and their multifaceted impacts and underscore the potential value of programmatic components explicitly designed to support clients from multiply disadvantaged backgrounds.
Authors: Oladunni Oluwoye; Liat Kriegel; Karl C Alcover; Michael T Compton; Leopoldo J Cabassa; Michael G McDonell Journal: Schizophr Res Date: 2020-01-03 Impact factor: 4.939
Authors: Nev Jones; Sarah Kamens; Oladunni Oluwoye; Franco Mascayano; Chris Perry; Marc Manseau; Michael T Compton Journal: Psychiatr Serv Date: 2021-01-12 Impact factor: 4.157
Authors: Donal O'Keeffe; Ann Sheridan; Aine Kelly; Roisin Doyle; Kevin Madigan; Elizabeth Lawlor; Mary Clarke Journal: Soc Psychiatry Psychiatr Epidemiol Date: 2021-06-18 Impact factor: 4.328