Gendered racial microaggressions and stress in PAs who are Black women.

OBJECTIVE: This mixed-methods study explored whether physician associates/assistants (PAs) who are Black women (for brevity, called Black women PAs throughout this article) experience gendered racial microaggressions and whether these experiences correlated with psychologic distress. The phrase Black women encompasses those who identify with the sociocultural roles, behaviors, and expressions of being a Black woman. METHODS: We conducted an online survey of Black women PAs using the Gendered Racial Microaggressions Scale during a 2-month period in 2019. RESULTS: Black women PAs experienced gendered racial microaggressions in clinical settings. Gendered racial microaggressions were correlated with stress, being silenced and marginalized, and assumptions of beauty and sexual objectification. No correlations were found between stress and the angry Black woman and strong Black woman variables. CONCLUSIONS: This study revealed that Black women have interlocking forms of oppression related to their race and gender, which are associated with psychologic distress. Awareness of these occurrences can reduce the unknowing perpetuation of gendered racial microaggressions and create cultural awareness practices.

Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy.

In the classical infectious disease compartment model, the parameters are fixed. In reality, the probability of virus transmission in the process of disease transmission depends on the concentration of virus in the environment, and the concentration depends on the proportion of patients in the environment. Therefore, the probability of virus transmission changes with time. Then how to fit the parameters and get the trend of the parameters changing with time is the key to predict the disease course with the model. In this paper, based on the US COVID-19 epidemic statistics during calibration period, the parameters such as infection rate and recovery rate are fitted by using the linear regression algorithm of machine science, and the laws of these parameters changing with time are obtained. Then a SIR model with time delay and vaccination is proposed, and the optimal control strategy of epidemic situation is analyzed by using the optimal control theory and Pontryagin maximum principle, which proves the effectiveness of the control strategy in restraining the transmission of COVID-19. The numerical simulation results show that the time-varying law of the number of active cases obtained by our model basically conforms to the real changing law of the US COVID-19 epidemic statistics during calibration period. In addition, we have predicted the changes in the number of active cases in the COVID-19 epidemic in the USA over time in the future beyond the calibration cycle, and the predicted results are more in line with the actual epidemic data.