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Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data

Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data

County B, on the east side of the state, is an urban county, with over 370,000 people. The jail had 404 beds and 8300 bookings in 2019. These jails vary in screening methods for suicide risk at jail booking, which reflects variability nationally. County A uses several scripted questions about current and past suicide ideation or attempts, and County B uses several scripted questions and a truncated version of the Columbia Suicide Severity Rating Scale [25]. The 2 jail populations vary demographically.

Erin B Comartin, Grant Victor, Athena Kheibari, Brian K Ahmedani, Bethany Hedden-Clayton, Richard N Jones, Ted R Miller, Jennifer E Johnson, Lauren M Weinstock, Sheryl Kubiak

JMIR Res Protoc 2025;14:e68517

Mobile Health Intervention Tools Promoting HIV Pre-Exposure Prophylaxis Among Adolescent Girls and Young Women in Sub-Saharan Africa: Scoping Review

Mobile Health Intervention Tools Promoting HIV Pre-Exposure Prophylaxis Among Adolescent Girls and Young Women in Sub-Saharan Africa: Scoping Review

Our literature search identified a number of m Health interventions, such as B-wise [80], a website with Pr EP information for adolescent girls and young women in South Africa, and Tutu Tester [81], a mobile clinic that used m Health to connect with HIV testers in the community [82,83]; however, these interventions were not included, as their impacts from m Health were not published in peer-reviewed journals or as conference abstracts.

Alex Emilio Fischer, Homaira Hanif, Jacob B Stocks, Aimee E Rochelle, Karen Dominguez, Eliana Gabriela Armora Langoni, H Luz McNaughton Reyes, Gustavo F Doncel, Kathryn E Muessig

JMIR Mhealth Uhealth 2025;13:e60819

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis

For example, consider a set of variables {A,B,C,D} that have the dependency relations described by the DAG A←B→C→D, then this DAG would be consistent with the partition {{B},{A,C},{D}}. The sampling procedure was run across 8 chains and checked for convergence and resolution (Note S2 in Multimedia Appendix 1). We used the Bayesian Gaussian equivalent score to retain the ordinal information of the random variables. Simulating outcomes given a DAG is performed by constructing a Bayesian network (BN).

Mathew Varidel, Victor An, Ian B Hickie, Sally Cripps, Roman Marchant, Jan Scott, Jacob J Crouse, Adam Poulsen, Bridianne O'Dea, Sarah McKenna, Frank Iorfino

J Med Internet Res 2025;27:e71305

Temporal Dynamics of Subtle Cognitive Change: Validation of a User-Friendly Multidomain Digital Assessment Using an Alcohol Challenge

Temporal Dynamics of Subtle Cognitive Change: Validation of a User-Friendly Multidomain Digital Assessment Using an Alcohol Challenge

(A) “Symbol Swap” DSST analog; (B) “Memory Match” visual associative learning task; (C) “Double-Take” N-back; and (D) “Rapid Response” simple reaction time test. DSST: Digit Symbol Substitution Task. Screenshots of the tablet app, including nontask elements. (A) Post-log-in landing screen; (B) task list view, showing the current/next task to be completed; (C) 1 of 5 instructions screens before Symbol Swap; (D) 1 of 4 instructions screens before Memory Match.

John Frederick Dyer, Florentine Marie Barbey, Ayan Ghoshal, Ann Marie Hake, Bryan J Hansen, Md Nurul Islam, Judith Jaeger, Rouba Kozak, Hugh Marston, Mark Moss, Viet Nguyen, Rebecca Louise Quinn, Leslie A Shinobu, Elizabeth Tunbridge, Brian Murphy, Niamh Kennedy

J Med Internet Res 2025;27:e55469