Implementing evidence-based supported employment within specialty mental health probation: A hybrid type I implementation-effectiveness trial protocol

在专业精神健康缓刑中实施循证支持就业:一项混合型 I 类实施效果试验方案

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Abstract

BACKGROUND: People with serious mental illnesses who are involved in the criminal justice system face significant challenges in obtaining meaningful employment. Given the importance of employment in reducing recidivism, the field needs effective interventions to increase employment rates, address mental health needs, and reduce recidivism for justice-involved people with serious mental illnesses. Individual Placement and Support - Supported Employment (IPS-SE) improves employment outcomes among individuals with serious mental illnesses and has shown promising results when implemented with individuals with histories of justice involvement; however, IPS-SE has only been implemented in mental health service settings. Given lower levels of treatment engagement and completion among justice-involved populations, implementation of IPS-SE in specialty mental health probation (SMHP) is an opportunity to increase reach and engagement among justice-involved people with serious mental illnesses. METHODS: This article describes a hybrid type 1 implementation-effectiveness study that aims to: (1) assess the implementation enablers and barriers, as well as the feasibility, appropriateness, and acceptability of IPS-SE embedded within SMHP; (2) identify the multi-level factors (i.e., implementation determinants) that influence IPS-SE implementation within the context of a probation setting; and (3) assess the impact of IPS-SE on employment - our primary endpoint - and the impact of IPS-SE on a number of secondary outcomes and potential treatment mechanisms. The efficacy arm of the study will be a randomized controlled trial of 130 adults on community supervision who will either receive treatment as usual or IPS-SE. The implementation arm of the study will examine implementation determinants and implementation outcomes using qualitative methods.

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