Publications by authors named "E Wartella"

The COVID-19 pandemic changed school contexts and social opportunities dramatically for adolescents around the world. Thus, certain adolescents may have been more susceptible to the stress of the pandemic as a function of differences in schooling. We present data from 1256 United States adolescents (ages 14-16) to examine how the 2020-2021 school context (in-person, hybrid, or virtual) related to feelings of school satisfaction and success, social connection, mental health, and media use.

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Addressing mental stigma is a key component of improving mental health outcomes. A digital media campaign was implemented to reduce mental health stigma in the Omaha Metropolitan area. The campaign used evidence-based approaches within a collective impact framework.

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Background: Low birthweight is a health issue disproportionately experienced by Black women. In Hillsborough County, Florida, Black women experience higher rates of low birthweight compared to the rest of Florida. This study examines the feasibility of a second attempt to use a digital low birthweight campaign to increase knowledge about low birthweight and pregnancy among Black women in Hillsborough.

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Background: A conflicting body of research suggests that additional investigation is needed to understand how globally watched television shows featuring social and mental health issues, such as 13 Reasons Why, might affect adolescents' mental wellness.

Objective: This study aims to investigate adolescents' viewership of the third season of the Netflix drama 13 Reasons Why (13RW-3) and their engagement with show-related content, paying special attention to mental health outcomes and conversational partners.

Methods: A panel-based research platform operated by the National Opinion Research Center at the University of Chicago recruited 157 adolescents aged 13 to 17 years from its nationally representative pool of participants.

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Objectives: To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation.

Methods: We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically.

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