Predicting Daily Sheltering Arrangements among Youth Experiencing Homelessness Using Diary Measurements Collected by Ecological Momentary Assessment

Robert Suchting, Michael S. Businelle, Stephen W. Hwang, Nikhil S. Padhye, Yijiong Yang and Diane M. Santa Maria
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Robert Suchting: Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA
Michael S. Businelle: The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
Stephen W. Hwang: MAP Centre for Urban Health Solutions, St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
Nikhil S. Padhye: Cizik School of Nursing, University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA
Yijiong Yang: Cizik School of Nursing, University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA
Diane M. Santa Maria: Cizik School of Nursing, University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA

IJERPH, 2020, vol. 17, issue 18, 1-17

Abstract: Youths experiencing homelessness (YEH) often cycle between various sheltering locations including spending nights on the streets, in shelters and with others. Few studies have explored the patterns of daily sheltering over time. A total of 66 participants completed 724 ecological momentary assessments that assessed daily sleeping arrangements. Analyses applied a hypothesis-generating machine learning algorithm (component-wise gradient boosting) to build interpretable models that would select only the best predictors of daily sheltering from a large set of 92 variables while accounting for the correlated nature of the data. Sheltering was examined as a three-category outcome comparing nights spent literally homeless, unstably housed or at a shelter. The final model retained 15 predictors. These predictors included (among others) specific stressors (e.g., not having a place to stay, parenting and hunger), discrimination (by a friend or nonspecified other; due to race or homelessness), being arrested and synthetic cannabinoids use (a.k.a., “kush”). The final model demonstrated success in classifying the categorical outcome. These results have implications for developing just-in-time adaptive interventions for improving the lives of YEH.

Keywords: youth experiencing homelessness; daily sleeping arrangement; electronic momentary assessment; machine learning; data science (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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