Sex Education on TikTok: A Content Analysis of Themes.

Leading medical and public health societies endorse comprehensive sex education, but only 20 states and Washington, D.C., currently require information about contraception when sex education is taught, and even fewer require the inclusion of topics such as gender diversity or consent. At the same time, social media use, especially the video-sharing app TikTok, is increasing among teens. TikTok, therefore, offers a novel opportunity to make up for shortcomings in sex education and convey sexual health information to adolescents. To describe the availability and content of sexual education on TikTok, we conducted a content analysis of themes for 100 sex education-focused videos. We found that female anatomy was the most frequently addressed topic. Sexual pleasure was the second most common theme, within which discussions of the female orgasm and arousal constituted the most common subtheme. Other common themes include contraception and sexual health. These sought-after topics may be incongruent with those presented in standard school- or home-based sex education or interactions with health care providers, and this disconnect suggests opportunities for health care providers and educators to initiate conversations or offer resources on these themes as part of routine interaction. We conclude with recommendations for future research to consider the factual accuracy of sex education on TikTok and determine how exposure to this content affects adolescents' understanding of the risks and benefits of intercourse, sexual practices, age- and gender-based sexual norms, and other health behaviors.

Parental Leave, Lactation, and Childcare Policies at Top US Schools of Public Health.

OBJECTIVES: To describe policies related to parental leave, breastfeeding, and childcare for faculty and staff at top schools of public health in the United States. METHODS: We identified the top 25 schools of public health from the US News and World Report rankings. We reviewed each institutional Web site to identify publicly available policies as of July 2018. RESULTS: For birth mothers, 80% (20/25) of the schools provided paid childbearing leave to faculty (mean = 8.2 weeks), and 48% (12/25) provided paid childbearing leave for staff (mean = 5.0 weeks). For nonbirth parents, 68% (17/25) provided paid parental leave for faculty and 52% (13/25) for staff (range = 1-15 weeks). We found that 64% (16/25) of the schools had publicly available lactation policies, and 72% (18/25) of the schools had at least 1 university-run on-campus childcare center. CONCLUSIONS: The majority of top US schools of public health provide paid leave to faculty birth mothers. However, most schools fall short of the 14 weeks recommended by the American Public Health Association.

Attitudes and Beliefs of Patients With Left-Ventricular Assist Devices Toward COVID-19 Vaccination and Willingness to Seek Care During the Pandemic.

Given the stalling improvement in vaccine hesitancy rates in the United States (US), it is important to understand why a chronically ill group, patients with left-ventricular assist devices (LVADs), might not get vaccinated and to delineate the barriers they may face in seeking care. We conducted an online survey to characterize the attitudes of patients with LVADs toward COVID-19 vaccination, identify their willingness to seek care during the pandemic, and characterize barriers to doing so. Our survey showed that the rate of vaccine hesitancy among LVAD patients is similar to that of the general population in the US. This rate is higher than expected for a chronically ill group at risk of severe COVID-19 infection. We also found that LVAD patients perceive barriers to seeking care during the pandemic. We recommend that LVAD care teams emphasize that patients should seek care for emergency medical conditions despite their fears of contracting COVID-19. Based on our results, we also recommend vaccine uptake education for this population focused on patients' concerns about serious side effects and not enough research done on the vaccine.

Mitigating Racial Bias in Machine Learning.

When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvantaged groups.