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Leveraging Electronic Health Records in International Humanitarian Clinics for Population Health Research: Cross-Sectional Study

Sarah Draugelis, Jessica Hunnewell, Sam Bishop, Reena Goswami, Sean G Smith, Philip Sutherland, Justin Hickman, Donald A Donahue, George A Yendewa, Amir M Mohareb

JMIR Public Health Surveill 2025;11:e66223

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Making Medical Education Courses Visible: Theory-Based Development of a National Database

Andi Gashi, Monika Brodmann Maeder, Eva K. Hennel

JMIR Med Educ 2025;11:e62838

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Using Music to Promote Hong Kong Young People’s Emotion Regulation and Reduce Their Mood Symptoms and Loneliness: Protocol for a Pilot Randomized Controlled Trial

Yuan Cao, Yuanxin Shi, Debbie Chi Wing Low, Daniel T L Shek, David H K Shum, Radhika Tanksale, Genevieve Dingle

JMIR Res Protoc 2025;14:e67764

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Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

We evaluated several recommender methods for accurate prediction, including K-nearest neighbors, probabilistic matrix factorization, collective matrix factorization, and the Bayesian probabilistic matrix factorization (BPMF) [33]. In evaluating rating prediction methods, we used a range of standard performance metrics including root mean squared error, Kendall tau-b, and normalized discounted cumulative gain. In all these tests, BPMF was identified as the best single model.

Ariana Kamberi, Benjamin Weitz, Julie Flahive, Julianna Eve, Reem Najjar, Tara Liaghat, Daniel Ford, Peter Lindenauer, Sharina Person, Thomas K Houston, Megan E Gauvey-Kern, Jackie Lobien, Rajani S Sadasivam