Self-directed violence (SDV), comprising both suicide and self-injury, presents a continued public health challenge for correctional institutions. In fact, correctional settings are one of four primary targets for the reduction of SDV by leading professional organizations. This article presents a public health solution to SDV in correctional settings, namely the Core Competency Model for Corrections (CCM for Corrections), an educational program for correctional mental health providers. Grounded in the general CCM of Suicide Prevention, we proffer an evidence-based sample curriculum covering 10 SDV prevention competencies in correctional settings. These competencies address both clinical care (e.g., enacting evidence-based treatment plans, using best practice documentation standards) and provider-focused (e.g., managing personal attitudes about SDV and incarcerated persons, engaging in self-care and debriefing) skills. We further espouse the underlying social-cognitive theory of CCM for Corrections toward the goal of identifying mechanisms of action for improved SDV prevention skills. Finally, we highlight considerations in the initial design and testing of CCM for Corrections. These recommendations address (a) utilization of a community–academic partnership approach and corrections SDV advisory panel, (b) selection of an in-person or online training modality, and (c) measurement of sample educational program outcomes. The CCM for Corrections represents a promising approach to SDV reduction and management in correctional settings ripe for collaborative pilot testing.

BackgroundSuicide deaths have been increasing for the past 20 years in the USA resulting in 45 979 deaths in 2020, a 29% increase since 1999. Lack of data linkage between entities with potential to implement large suicide prevention initiatives (health insurers, health institutions and corrections) is a barrier to developing an integrated framework for suicide prevention.ObjectivesData linkage between death records and several large administrative datasets to (1) estimate associations between risk factors and suicide outcomes, (2) develop predictive algorithms and (3) establish long-term data linkage workflow to ensure ongoing suicide surveillance.MethodsWe will combine six data sources from North Carolina, the 10th most populous state in the USA, from 2006 onward, including death certificate records, violent deaths reporting system, large private health insurance claims data, Medicaid claims data, University of North Carolina electronic health records and data on justice involved individuals released from incarceration. We will determine the incidence of death from suicide, suicide attempts and ideation in the four subpopulations to establish benchmarks. We will use a nested case–control design with incidence density-matched population-based controls to (1) identify short-term and long-term risk factors associated with suicide attempts and mortality and (2) develop machine learning-based predictive algorithms to identify individuals at risk of suicide deaths.DiscussionWe will address gaps from prior studies by establishing an in-depth linked suicide surveillance system integrating multiple large, comprehensive databases that permit establishment of benchmarks, identification of predictors, evaluation of prevention efforts and establishment of long-term surveillance workflow protocols.

Objective: We developed the Self-Injury Risk Assessment Protocol for Corrections (SIRAP-C) to meet legal mandates for self-directed violence (SDV) risk assessment standards in correctional settings. We focused on two empirical aims: (1) factor structure and internal consistency and (2) subscale associations with SDV and intervention recommendation outcomes. Hypotheses: We expected a multifactorial SIRAP-C structure with acceptable internal consistency. We further expected SIRAP-C subscales would distinguish history of SDV events while incarcerated, current SDV event category, and treatment recommendation. Method: We drew electronic health record data for adult incarcerated persons (N = 3,929) from state Division of Prisons records from 2016 to 2020. Clinical records included demographic and correctional institutional information, as well as SIRAP-C records. Factor analyses assessed Aim 1. Regression models tested Aim 2. Results: Factor analyses supported a seven-factor SIRAP-C structure (27 items) comprising Depressive Symptoms, Reasons for Living, History of Self-Directed Violence, Current Suicidal Thinking, Family History of Self-Directed Violence, Coping Skills, and Social Connectedness. Subscales displayed acceptable internal consistency, with the exception of social connectedness in the confirmatory factor analysis subsample. Lower depressive symptoms and coping skills, as well as higher history of SDV, were associated with increased risk for a prior SDV assessment event while incarcerated. Lower depressive symptoms, current suicidal thinking, and coping skills and higher history of SDV marked worse risk for self-injurious behavior. Higher depressive symptoms and current suicidal thinking, as well as lower reasons for living, demarcated suicidal acts from self-injury. Higher history of SDV and lower coping skills indicated outpatient/residential treatment. Elevated depressive symptoms and history of SDV, as well as lower reasons for living and coping skills, were associated with inpatient hospitalization. Conclusions: The SIRAP-C represents a promising clinical approach advancing correctional SDV risk assessment. We offer future research, policy, and implementation recommendations. Public Significance StatementSelf-directed violence, including both suicide and self-injury, is a pressing problem in correctional settings, yet there is very little in the way of evidence-based approaches to managing and assessing risk for these issues. Such an instrument could provide correctional mental health providers with a structured approach to deliver sound care for vulnerable persons in correctional institutions. Although the assessment instrument we present is a promising start, future research and implementation should build on its development.