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After that, the DN had a brief follow-up discussion (approximately 5 min) with participants to provide ongoing support and complete data collection.
Textbox 1 details the discussion topics that guided the study visits between the DN and participants, and Multimedia Appendix 1 collates the most common questions asked by the DN.
JMIR Ment Health 2025;12:e70154
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For survey data preprocessing, we standardized continuous variables using a Min-Max scaler and applied one-hot encoding to categorical variables. For actigraphy data, temporal pattern characteristics were extracted using an autoencoder. An autoencoder is a neural network composed of an encoder and a decoder, which enables automatic feature learning from unlabeled data [58]. Eight actigraphy features were extracted across the 4 designated periods.
J Med Internet Res 2025;27:e69379
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Knowledge of end-stage dementia and ACP
Decisional conflict
Difference in knowledge of end-stage dementia treatment: 6.38 (SD 4.16) versus 8.75 (SD 4.74); Cohen d=0.5
Difference in knowledge of ACP: 2.95 (SD 3.49) versus 5.33 (SD 4.20); Cohen d=0.6
Difference in decisional conflict scores: 49.88 (SD 21.03) versus 35.11 (SD 16.62); Cohen d=−0.8
PROVENf: 5 previously created videos (6- to 10-min long) offered in English or Spanish (General Goals of Care, Goals of Care for Advanced Dementia, Hospice, Hospitalization
J Med Internet Res 2025;27:e71479
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On average, trained clinicians spent 36.7% (219.6 min) less time using the Epic system per day than controls at 3 months postcourse (Figure 2). Time spent in the Notes module per day was 56.6% (29 min) lower for trained clinicians than controls at 3 months post-course (Figure 3).
JMIR Form Res 2025;9:e68491
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