Acceptability of Telehealth for Gender-Affirming Care in Transgender and Gender Diverse Youth and Their Caregivers.

Gender-affirming care (GAC) is critical to the well-being of transgender and gender diverse youth and was limited by COVID-19 stay-at-home orders. Telehealth created opportunities for youth to continue receiving lifesaving care. We examined the attitudes of patients (n=21) and caregivers (n=38) receiving telehealth-delivered GAC (TGAC) from May to July 2020. Participants completed surveys after telehealth visits. Descriptive statistics compared telehealth with in-person visits across key domains. Overall, 86.5% of patients and 95.4% of caregivers were satisfied with medical TGAC and 94.3% and 93.3% were satisfied with behavioral health TGAC. Future research should determine the effectiveness of TGAC and identify areas for improvement.

Experiences of violence and discrimination among LGBTQ+ individuals during the COVID-19 pandemic: a global cross-sectional analysis.

OBJECTIVES: To characterise the extent to which the levels of violence and discrimination against lesbian, gay, bisexual, transgender and queer (LGBTQ+) people have changed amid COVID-19. DESIGN: Cross-sectional, secondary analysis. SETTING: 79 countries. PARTICIPANTS: All adults (aged ≥18 years) who used the Hornet social networking application and provided consent to participate. MAIN OUTCOME MEASURE: The main outcome was whether individuals have experienced less, or the same or more levels of discrimination and violence from specific groups (eg, police and/or military, government representatives, healthcare providers). RESULTS: 7758 LGBTQ+ individuals provided responses regarding levels of discrimination and violence. A majority identified as gay (78.95%) and cisgender (94.8%). Identifying as gay or queer was associated with increased odds of experiencing the same or more discrimination from government representatives (OR=1.89, 95% CI 1.04 to 3.45, p=0.045) and healthcare providers (OR=2.51, 95% CI 0.86 to 7.36, p=0.002) due to COVID-19. Being a member of an ethnic minority was associated with increased odds of discrimination and violence from police and/or military (OR=1.32, 95% CI 1.13 to 1.54, p=0.0) and government representatives (OR=1.47, 95% CI 1.29 to 1.69, p=0.0) since COVID-19. Having a disability was significantly associated with increased odds of violence and discrimination from police and/or military (OR=1.38, 95% CI 1.15 to 1.71, p=0.0) and healthcare providers (OR=1.35, 95% CI 1.07 to 1.71, p=0.009). CONCLUSIONS: Our results suggest that despite the upending nature of the COVID-19 pandemic, around the world, government representatives, policymakers and healthcare providers continue to perpetuate systemic discrimination and fail to prevent violence against members of the LGBTQ+ community.

Evaluating criminal justice reform during COVID-19: The need for a novel sentiment analysis package.

The health and safety of incarcerated persons and correctional personnel have been prominent in the U.S. news media discourse during the COVID-19 pandemic. Examining changing attitudes toward the health of the incarcerated population is imperative to better assess the extent to which the general public favors criminal justice reform. However, existing natural language processing lexicons that underlie current sentiment analysis (SA) algorithms may not perform adequately on news articles related to criminal justice due to contextual complexities. News discourse during the pandemic has highlighted the need for a novel SA lexicon and algorithm (i.e., an SA package) tailored for examining public health policy in the context of the criminal justice system. We analyzed the performance of existing SA packages on a corpus of news articles at the intersection of COVID-19 and criminal justice collected from state-level outlets between January and May 2020. Our results demonstrated that sentence sentiment scores provided by three popular SA packages can differ considerably from manually-curated ratings. This dissimilarity was especially pronounced when the text was more polarized, whether negatively or positively. A randomly selected set of 1,000 manually scored sentences, and the corresponding binary document term matrices, were used to train two new sentiment prediction algorithms (i.e., linear regression and random forest regression) to verify the performance of the manually-curated ratings. By better accounting for the unique context in which incarceration-related terminologies are used in news media, both of our proposed models outperformed all existing SA packages considered for comparison. Our findings suggest that there is a need to develop a novel lexicon, and potentially an accompanying algorithm, for analysis of text related to public health within the criminal justice system, as well as criminal justice more broadly.