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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

Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach

Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach

(B) Average graph of mobile EMA responses for the loneliness levels of 3 clusters. EMA: ecological momentary assessment. We used leave-some-out cross-validation to construct the training and validation data sets. The complete data set of 99 samples was divided into 10 folds using stratified sampling, separately for low social interaction frequency and high levels of loneliness.

Bada Kang, Min Kyung Park, Jennifer Ivy Kim, Seolah Yoon, Seok-Jae Heo, Chaeeun Kang, SungHee Lee, Yeonkyu Choi, Dahye Hong

J Med Internet Res 2025;27:e69379

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

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study

SHAP (Shapley Additive Explanations) summary plot of the top 20 features in CC prediction using best-performing models with 0-year and 3-year prediction windows: (A) excluding CRC-related features; (B) excluding cancer-related features. The prefix before the “_” in the y-axis labels of plots indicates the source of the corresponding features in the PCORnet data model. Specifically, these sources are: Diagnosis (Diag), Procedure (Proc), Medication (Med), Vital Signs (Vital), and Demographics (Demo).

Chengkun Sun, Erin Mobley, Michael Quillen, Max Parker, Meghan Daly, Rui Wang, Isabela Visintin, Ziad Awad, Jennifer Fishe, Alexander Parker, Thomas George, Jiang Bian, Jie Xu

JMIR Cancer 2025;11:e64506