Intervention for Justice-Involved Homeless Veterans With Co-Occurring Substance Use and Mental Health Disorders: Protocol for a Randomized Controlled Hybrid Effectiveness-Implementation Trial
Bibliographic record
Abstract
BACKGROUND: The US Veterans Affairs mental health residential rehabilitation treatment programs (MH RRTPs) provide residential care for veterans experiencing homelessness. However, those with co-occurring mental health and substance use disorders and criminal legal involvement require additional interventions to address risk factors for recidivism. OBJECTIVE: We aimed to (1.1) evaluate whether the Maintaining Independence and Sobriety through Systems Integration, Outreach, and Networking Criminal Justice version (MISSION-CJ) intervention lowers criminal recidivism and improves health-related outcomes; (1.2) examine the mechanisms that impact outcomes; and (2) qualitatively assess the implementation of MISSION-CJ. METHODS: Veterans participating in an MH RRTP (N=226) will be randomized to the enhanced usual care (EUC) or MISSION-CJ conditions in a hybrid type 1 randomized controlled trial to test the effectiveness and implementation of MISSION-CJ, a multicomponent intervention for co-occurring disorder. Both conditions will receive 6 months of services beginning within a week of MH RRTP enrollment (duration of stay: 3 months) and continue for 3 months after the MH RRTP in the community. The veterans in the EUC group (113/226, 50%) will receive a peer support curriculum and community outreach and linkage delivered by a peer support specialist. The veterans in the MISSION-CJ group (113/226, 50%) will receive team-based (case manager and peer support specialist) care, including treatment planning, case management using a critical time intervention model to promote referrals and linkages, enhanced dual recovery therapy sessions, and peer support sessions. Assessments, including questions regarding substance use and mental health history, criminal history and recidivism risk, housing, employment, medication adherence, mutual-help group attendance, antisocial attitudes, affiliations with peers, community involvement, and treatment services received, will be conducted at baseline and 6 months and 15 months after baseline. We will use generalized linear mixed effects regression models to evaluate MISSION-CJ based on outcomes (objective 1.1). We will conduct mediation analysis to examine mechanisms of action (objective 1.2). For the qualitative evaluation (objective 2), we will use thematic analysis to identify themes. RESULTS: As of March 2025, 118 veterans (site 1: n=52, 44.1% and site 2: n=66, 55.9%) have been enrolled. Overall, 58 veterans (site 1: n=27, 47% and site 2: n=31, 53%) have been randomized to the MISSION-CJ group, and 60 veterans (site 1: n=25, 42% and site 2: n=35, 58%) have been randomized to the EUC group. Overall, 23 interviews for the qualitative evaluation have been completed with veterans. Veterans are continuing to receive treatment and completing follow-up assessments. The findings from this trial and qualitative evaluation will be available by 2026. The quantitative and qualitative components of this project are intended to work synergistically to reinforce knowledge of MISSION-CJ's effectiveness, implementation, and scalability. CONCLUSIONS: If effective, the implementation of MISSION-CJ alongside the MH RRTPs may be advantageous to address risk factors related to recidivism. TRIAL REGISTRATION: ClinicalTrials.gov NCT04523337; https://clinicaltrials.gov/study/NCT04523337. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/70750.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.083 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".