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The Prevention of Depression: A Machine Learning Approach
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | The Prevention of Depression: A Machine Learning Approach. |
| 개인저자 | Fuss, Ashley Ann. |
| 단체저자명 | University of Pennsylvania. Social Welfare. |
| 발행사항 | [S.l.]: University of Pennsylvania., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 136 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02A. Dissertation Abstract International |
| ISBN | 9781085624336 |
| 학위논문주기 | Thesis (Ph.D.)--University of Pennsylvania, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: A.
Advisor: Engstrom, Malitta. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Behavioral health disorders, specifically depression, are a serious health concern in the United States and worldwide. The consequences of unaddressed behavioral health conditions are multifaceted and have impact at the individual, relational, communal, and societal level. Despite the number of individuals who could benefit from treatment for behavioral health concerns, their difficulties are often unidentified and unaddressed through treatment. Technology carries unrealized potential to identify people at risk for behavioral health conditions and to inform prevention and intervention strategies. Drawing upon data from the National Longitudinal Study of Adolescent Health (Add Health, n=3782), this study has two aims related to advancing understanding of technology's potential value in behavioral health: 1) to develop a forecasting procedure that can be used to identify youth who are at risk of reporting a depression diagnosis as adults based on a set of input variables |
| 일반주제명 | Social work. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
