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Intervention for Justice-Involved Homeless Veterans With Co-Occurring Substance Use and Mental Health Disorders: Protocol for a Randomized Controlled Hybrid Effectiveness-Implementation Trial

Intervention for Justice-Involved Homeless Veterans With Co-Occurring Substance Use and Mental Health Disorders: Protocol for a Randomized Controlled Hybrid Effectiveness-Implementation Trial

In the first model, we will regress the outcome variable of interest (eg, recidivism and the Level of Service Inventory–Revised [LSI-R] total scores) on the MISSION-CJ treatment condition. In the second model, we will regress the potential mediator of interest on the MISSION-CJ treatment condition. In the third model, we will regress the outcome of interest (eg, recidivism and the LSI-R total scores) on the potential mediator.

Kathryn Bruzios, Paige M Shaffer, Daniel M Blonigen, Michael A Cucciare, Michael Andre, Thomas Byrne, Jennifer Smith, David Smelson

JMIR Res Protoc 2025;14:e70750

Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation Study

Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation Study

Finally, using built-in functionality in the STM R package, we avoided subjective decisions on the number of topics by using a data-driven approach [20]. This approach helped eliminate human-based bias in our analysis by identifying words that appear in a document only if the document pertains to a specific topic.

Priscilla Pemu, Michael Prude, Atuarra McCaslin, Elizabeth Ojemakinde, Christopher Awad, Kelechi Igwe, Anny Rodriguez, Jasmine Foriest, Muhammed Idris

JMIR Form Res 2025;9:e65320