The Influence of Pre-Migration Factors and Post-Migration Climate of the Receiving Community on the Psychological Distress of Latino Immigrants

Over forty million foreign-born residents currently live in the United States. Latinos make up the largest population of immigrants living in the U.S. Previous research suggests that Latino immigrants often experience pre-migration stressors, such as traumatic experiences, political upheaval, and unplanned migration. These stressors may have a negative impact on immigrants’ post-migration mental health. Research also suggests that the post-migration climate of the receiving community may inform the connection between pre-migration experiences and post-migration mental health. The current study examined the relationship between Latino immigrants’ reasons for migration, migration planning, and pre-migration experience of political and/or interpersonal violence, and post-migration symptoms of psychological distress. In addition to examining the effect of these pre-migration factors, the current study also examined the community “climate” experienced by Latino immigrants post-migration by assessing the influence of three post-migration factors: 1) community support and engagement, 2) discrimination, and 3) employment. The study was a secondary analysis of data collected for the National Latino and Asian American Study, which focused on the mental health and service utilization of Latinos and Asian Americans. Participants included 1,629 Latino immigrants from across the United States. Results indicated that pre-migration experience of political and/or interpersonal trauma, post-migration experience of discrimination, and female sex were positively associated with psychological distress. Post-migration employment was negatively associated with psychological distress. In addition, discrimination modified the association between unplanned migration and psychological distress; the relationship between unplanned migration and psychological distress decreased for participants who reported more discrimination. Furthermore, employment modified the association between political and/or interpersonal trauma and psychological distress; the connection between trauma and psychological distress increased among those who reported having less employment. Recommendations for further research were presented. Policy and clinical practice implications were discussed, particularly given the current climate of high anti-immigrant sentiment and hostility in the U.S.

Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text

Human relationships have long been studied by scientists from domains like sociology, psychology, literature, etc. for understanding people's desires, goals, actions and expected behaviors. In this dissertation we study inter-personal relationships as expressed in natural language text. Modeling inter-personal relationships from text finds application in general natural language understanding, as well as real-world domains such as social networks, discussion forums, intelligent virtual agents, etc. We propose that the study of relationships should incorporate not only linguistic cues in text, but also the contexts in which these cues appear. Our investigations, backed by empirical evaluation, support this thesis, and demonstrate that the task benefits from using structured models that incorporate both types of information. We present such structured models to address the task of modeling the nature of relationships between any two given characters from a narrative. To begin with, we assume that relationships are of two types: cooperative and non-cooperative. We first describe an approach to jointly infer relationships between all characters in the narrative, and demonstrate how the task of characterizing the relationship between two characters can benefit from including information about their relationships with other characters in the narrative. We next formulate the relationship-modeling problem as a sequence prediction task to acknowledge the evolving nature of human relationships, and demonstrate the need to model the history of a relationship in predicting its evolution. Thereafter, we present a data-driven method to automatically discover various types of relationships such as familial, romantic, hostile, etc. Like before, we address the task of modeling evolving relationships but don't restrict ourselves to two types of relationships. We also demonstrate the need to incorporate not only local historical but also global context while solving this problem. Lastly, we demonstrate a practical application of modeling inter-personal relationships in the domain of online educational discussion forums. Such forums offer opportunities for its users to interact and form deeper relationships. With this view, we address the task of identifying initiation of such deeper relationships between a student and the instructor. Specifically, we analyze contents of the forums to automatically suggest threads to the instructors that require their intervention. By highlighting scenarios that need direct instructor-student interactions, we alleviate the need for the instructor to manually peruse all threads of the forum and also assist students who have limited avenues for communicating with instructors. We do this by incorporating the discourse structure of the thread through latent variables that abstractly represent contents of individual posts and model the flow of information in the thread. Such latent structured models that incorporate the linguistic cues without losing their context can be helpful in other related natural language understanding tasks as well. We demonstrate this by using the model for a very different task: identifying if a stated desire has been fulfilled by the end of a story.