ABSTRACT
In a national sample of 565 Asian Americans, this study investigates whether direct experiences of coronavirus disease (COVID-19) racism or vicarious exposure (e.g., witnessing, news reports, videos, and stories) of others experiencing COVID-19 racism lead to adverse mental health outcomes above and beyond the impact of general COVID-19 stressors. We used moderated moderation models to test our hypotheses of main effects and three-way interaction effects using the PROCESS Macro in SPSS (Hayes, 2017). Our results showed that both direct and vicarious experience of COVID-related racism significantly and positively predicted depressive, anxiety, and somatic symptoms, above and beyond the impact of general COVID-related stressors. In addition, the three-way interaction of COVID-related racism, internalized racism, and generational status was significant in three out of four models. There was a significant interaction effect such that higher levels of internalized racism mitigated the strength of the relation of COVID-related direct and vicarious racism on depression and anxiety symptoms for 1.5 and 2nd+ generation Asian Americans. For 1st generation Asian Americans, internalized racism exacerbated the impact of vicarious COVID-related racism on somatic symptoms. Our findings revealed the nature of how immigration generational status and internalized racism moderated the relations between COVID-related racism and psychological wellbeing for Asian Americans during a public health crisis. (PsycInfo Database Record (c) 2023 APA, all rights reserved) Impact Statement What is the public significance of this article?-The study shows that direct and vicarious COVID-19-related racism was detrimental to Asian Americans' mental health. Additionally, the study revealed that the risk for adverse mental health outcomes depends on internalized racism beliefs and generational status, jointly. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
BACKGROUND: Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. METHODS AND FINDINGS: Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emergency department and a follow-up outpatient visit), 29% required advanced imaging, 6% required inpatient admission, and 2 died. We estimate 115 injuries per million e-scooter trips were treated in our health system. CONCLUSIONS: Our observed e-scooter injury rate is likely an underestimate, but is similar to that previously reported for motorcycles. However, the comparative severity of injuries is unknown. Our methodology may prove useful to study other clinical conditions not identifiable by existing diagnostic systems.