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From its inception, the public safety net in the United States has excluded many people because of their race, gendered roles, or other factors. As a result, they must prove their moral worthiness to get resources for themselves and their families. In When Care Is Conditional, sociologist Dani Carrillo reveals the ramifications of this conditional safety net by focusing on one particularly vulnerable population: undocumented immigrants. Through in-depth interviews with Latinx immigrants in northern California, Carrillo examines three circumstances—place, gender, and immigration status—that intersect to influence an individual’s access to health care, food assistance, and other benefits. She demonstrates that place of residence affects undocumented immigrants’ ability to get care since more services are available in urban areas, where many immigrants cannot afford to live, than suburban areas, where public transportation is limited. She also shows that while both men and women who are undocumented have difficulty obtaining care, men often confront more challenges. Undocumented women who are pregnant or mothers are eligible for some government safety net programs and rely on informal coethnic networks or a “guiding figure”—a relative, friend, neighbor, or coworker—who explains how to get care and makes them feel confident in accessing it. Most undocumented men, in contrast, are not eligible for public programs except in a medical emergency and often lack someone to guide them directly to care. Men sometimes steer one another to jobs through worker centers—where they may learn about various services and take advantage of those that increase their employability, like English or computer classes—but a culture of masculinity leads them to downplay medical problems and seek health care only in a crisis. As undocumented immigrants navigate this exclusionary system, Carrillo finds that they resist the rhetoric stigmatizing them as lawbreakers. Dismissing the importance of “papers” and highlighting their work ethic, they question the fairness of U.S. immigration policies and challenge ideas about who deserves care. Carrillo offers concrete recommendations, such as improving labor conditions and reexamining benefit eligibility, to increase access to care for not only undocumented immigrants but also people who have been excluded because of their race, criminal record, gender identity, sexual orientation, or disability. She argues that working with and across populations creates a powerful form of solidarity in advocating for inclusive care. When Care Is Conditional provides compelling insights into how safety net and immigration policies intersect to affect people’s everyday lives and calls for a cultural shift so that the United States can provide unconditional care for all.
A New York Times Editors' Choice • Best Book of the Year: Time, NPR, Bookpage, L.A. Times What does it mean to be American? In this starkly illuminating and impassioned book, Pulitzer Prize­­–finalist Laila Lalami recounts her unlikely journey from Moroccan immigrant to U.S. citizen, using it as a starting point for her exploration of American rights, liberties, and protections. "Sharp, bracingly clear essays."—Entertainment Weekly Tapping into history, politics, and literature, she elucidates how accidents of birth—such as national origin, race, and gender—that once determined the boundaries of Americanness still cast their shadows today. Lalami poignantly illustrates how white supremacy survives through adaptation and legislation, with the result that a caste system is maintained that keeps the modern equivalent of white male landowners at the top of the social hierarchy. Conditional citizens, she argues, are all the people with whom America embraces with one arm and pushes away with the other. Brilliantly argued and deeply personal, Conditional Citizens weaves together Lalami’s own experiences with explorations of the place of nonwhites in the broader American culture.
The Ethics of Conditional Confidentiality: A Practice Model for Mental Health Professionals is a guidebook designed to help therapists and other mental health professionals navigate the ethical and legal maze surrounding confidentiality.
Currently, there are over 400,000 youth living in foster care in the United States, with over 20,000 aging out of the child welfare system each year. Foster youth are more prone to experience short- and long-term adverse developmental outcomes including diminished academic achievement and career opportunities, poor mental and overall health, financial struggles, homelessness, early sexual intercourse, and substance abuse, many of these outcomes are risk factors for involvement in the juvenile justice system. Despite their challenges, foster youth have numerous strengths and positive assets that carry them through their journeys, helping them to overcome obstacles and build resilience. The Handbook of Foster Youth brings together a prominent group of multidisciplinary experts to provide nuanced insights on the complex dynamics of the foster care system, its impact on youth’s lives, and the roles of institutions and policies in the foster system. It discusses current gaps and future directions as well as recommendations to advance the field. This book provides an opportunity to reflect on the many challenges and strengths of foster youth and the child welfare system, and the combined efforts of caregivers, community volunteers, policy makers, and the professionals and researchers who work with them.
Acclaimed for its thorough presentation of mediation, moderation, and conditional process analysis, this book has been updated to reflect the latest developments in PROCESS for SPSS, SAS, and, new to this edition, R. Using the principles of ordinary least squares regression, Andrew F. Hayes illustrates each step in an analysis using diverse examples from published studies, and displays SPSS, SAS, and R code for each example. Procedures are outlined for estimating and interpreting direct, indirect, and conditional effects; probing and visualizing interactions; testing hypotheses about the moderation of mechanisms; and reporting different types of analyses. Readers gain an understanding of the link between statistics and causality, as well as what the data are telling them. The companion website (www.afhayes.com) provides data for all the examples, plus the free PROCESS download. New to This Edition *Rewritten Appendix A, which provides the only documentation of PROCESS, including a discussion of the syntax structure of PROCESS for R compared to SPSS and SAS. *Expanded discussion of effect scaling and the difference between unstandardized, completely standardized, and partially standardized effects. *Discussion of the meaning of and how to generate the correlation between mediator residuals in a multiple-mediator model, using a new PROCESS option. *Discussion of a method for comparing the strength of two specific indirect effects that are different in sign. *Introduction of a bootstrap-based Johnson–Neyman-like approach for probing moderation of mediation in a conditional process model. *Discussion of testing for interaction between a causal antecedent variable [ital]X[/ital] and a mediator [ital]M[/ital] in a mediation analysis, and how to test this assumption in a new PROCESS feature.
When it’s exam time you need the right information in the right format to study efficiently and effectively. Emanuel® CrunchTime is the perfect tool for exam studying. With flowcharts and capsule summaries of major points of law and critical issues, as well as exam tips for identifying common traps and pitfalls, sample exam and essay questions with model answers – you will be prepared for your next big test. Here's why you will need Emanuel® CrunchTime to help you ace your exams: Perfect for the visual learner: The flow charts walk you through a series of yes/no questions that can be used to analyze any question on the exam. Featured capsule summaries help you quickly review key concepts not just before the exam, but throughout the semester Exams Tips recap the most commonly tested issues and fact patterns.
This volume contains the papers preesented at the Third International Workshop on Conditional Term Rewriting Systems, held in Pont- -Mousson, France, July 8-10, 1992. Topics covered include conditional rewriting and its applications to programming languages, specification languages, automated deduction, constrained rewriting, typed rewriting, higher-order rewriting, and graph rewriting. The volume contains 40 papers, including four invited talks: Algebraic semantics of rewriting terms and types, by K. Meinke; Generic induction proofs, by P. Padawitz; Conditional term rewriting and first-order theorem proving, by D. Plaisted; and Decidability of finiteness properties (abstract), by L. Pacholski. The first CTRS workshop was held at the University of Paris in 1987 and the second at Concordia University, Montreal, in 1990. Their proceddings are published as Lecture Notes in Computer Science Volumes 308 and 516 respectively.
This Research Topic is the fourth volume of the series Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine Volume I: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume I Volume II:Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume II Volume III:Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume III Analytics based on artificial intelligence has greatly advanced scientific research fields like natural language processing and imaging classification. Clinical research has also greatly benefited from artificial intelligence. Emergency and critical care physicians face patients with rapidly changing conditions, which require accurate risk stratification and initiation of rescue therapy. Furthermore, critically ill patients, such as those with sepsis, acute respiratory distress syndrome, and trauma, are comprised of heterogeneous population. The “one-size-fit-all” paradigm may not fit for the management of such heterogeneous patient population. Thus, artificial intelligence can be employed to identify novel subphenotypes of these patients. These sub classifications can provide not only prognostic value for risk stratification but also predictive value for individualized treatment. With the development of transcriptome providing a large amount of information for an individual, artificial intelligence can greatly help to identify useful information from high dimensional data. Altogether, it is of great importance to further utilize artificial intelligence in the management of critically ill patients.