Mexicans in the U.S and HIV: Reviewing Social and Cultural Factors

Mexican immigrants in the United States continue to struggle with disproportionate incidences of HIV infection. Behaviors that contributed to the development of AIDS among Mexican immigrants were the lack of condom use, engaging in risky sexual behavior, and not having HIV testing (Martínez Donate, et al., 2015). While these risk factors are preventable, religious and social stigmatization present within the Mexican community generate shame and embarrassment. This discourages Mexican immigrants from seeking health services, such as HIV testing. An estimated 20% of HIV positive Mexican immigrants are unaware of their condition, and contribute to the spread of the virus (Hall HI, et al., 2013). This paper will examine methods that address higher incidences of risky behaviors that contribute to the transmission of HIV within the Mexican immigrant population. Examining Marianismo, Machismo, Familismo, and Curanderismo’s influence on gender roles, provides insight towards the complexity of how attitudes towards sexuality, fidelity, and lay definitions of health within the context of the American health care system is crucial to successfully treat this particular population. Mexican immigrants in the United States continue to face obstacles that disproportionately affect their reception to HIV prevention education. Developing strategies that overcome these challenges should incorporate consideration of cultural beliefs and social constructs that could inhibit reception to sex education and treatment. Creating new HIV education programs that engage positive social values present within Mexican immigrant communities has the potential for successfully reducing HIV infection

The Scikit-HEP Project

The Scikit-HEP project is a community-driven and community-oriented effort with the aim of providing Particle Physics at large with a Python scientific toolset containing core and common tools. The project builds on five pillars that embrace the major topics involved in a physicist's analysis work: datasets, data aggregations, modelling, simulation and visualisation. The vision is to build a user and developer community engaging collaboration across experiments, to emulate scikit-learn's unified interface with Astropy's embrace of third-party packages, and to improve discoverability of relevant tools.Comment: 6 pages, 2 figures, Proceedings of the 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), 9-13 July 201