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2018

The accuracy of passive phone sensors in predicting daily mood

Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire-2. A machine learning approach was used to predict daily mood for the entire sample and individual participants.

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Harnessing the potential of administrative data to inform child welfare programming with dynamic visualization methodologies

The majority U.S. states maintain administrative databases to collect information on the entry, movement, and exits of youth in the foster care system, yet the power of these data to inform continuous improvement efforts remains largely untapped. This underutilization ignores the vast potential inherent in longitudinal child welfare data to better understand the trajectories of youth in care and the effectiveness of the services they receive.

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Last Updated: 4/14/21