Work

Using Machine Learning to Predict Young People’s Internet Health and Social Service Information Seeking

Year: 2021

Type: article

Study design: Observational Study

Source: Prevention Science

DOI: 10.1007/s11121-021-01255-2

Authors: W. Scott Comulada, Cameron Goldbeck, Ellen Almirol, Heather J. Gunn, Manuel A. Ocasio, M. Isabel Fernández, Elizabeth Mayfield Arnold, Adriana Romero-Espinoza, Stacey Urauchi, Wilson Ramos, Mary Jane Rotheram-Borus, Jeffrey D. Klausner, Dallas Swendeman, Adolescent Medicine Trials Network (ATN) CARES Team

Institutions: University of California, Los Angeles, Tulane University, Nova Southeastern University, The University of Texas Southwestern Medical Center

Language: English

Topic: Health Literacy and Information Accessibility

Subfield: General Health Professions

Field: Health Professions

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: green

Funders: National Institute of Mental Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: G5137042071, Post-doctoral HIV Research Training Program for HIV Combination Prevention, Center for HIV Identification, Prevention and Treatment Services

OpenAlex ID: W3163534539

FWCI: 1.586

Cites: 44

Cited by: 14

Related to: 10