Burnout, Secondary Traumatic Stress, and Compassion Fatigue: Employees of Anti-sex-trafficking Agencies Who Work Directly With Rescued Sex-trafficked Women

This phenomenological study aims to understand the shared experience of employees who work directly with rescued sex-trafficked women at anti-trafficking agencies in the United States. Chapter One details the theoretical contexts, including Maslow’s (1954) hierarchy of needs, Maslach’s (1982) cost of caring: burnout, McCann and Pearlman’s (1990) construct of vicarious traumatization, Rotter’s (1954) social learning theory as it relates to the impact of working with severely traumatized people. The problem statement is explained as the effectiveness of anti-sex trafficking agencies being influenced by the staff who provide care to rescued sex trafficked women, and there is currently little to no research on them. This study aims to identify the experiences of anti-sex trafficking agency employees who work directly with rescued sex-trafficked women and bring awareness to the effects of being employed in this field. Chapter 2 is an overview of the current literature on this topic. Because the research is underdeveloped in this field, the literature review focuses on the complexities of working with rescued sex-trafficked women and burnout components in similar occupations. Chapter 3 explains the phenomenological design, participants, and procedures. Data was collected through interviews, focused groups, and document reviews. Data was analyzed by looking for the meaning in patterns, themes, and categories found in the data. Chapter 4 describes the particpants, presents the results of the study,and addresses the research questions. Chapter 5 provides a summary of the findings and is followed by the discussions and implications for policy and practice. Next the chapter offers delimitations and limitations, followed by recommendations for further research

How humans learn and represent networks

Humans communicate, receive, and store information using sequences of items -- from words in a sentence or notes in music to abstract concepts in lectures and books. The networks formed by these items (nodes) and the sequential transitions between them (edges) encode important structural features of human communication and knowledge. But how do humans learn the networks of probabilistic transitions that underlie sequences of items? Moreover, what do people's internal maps of these networks look like? Here, we introduce graph learning, a growing and interdisciplinary field focused on studying how humans learn and represent networks in the world around them. We begin by describing established results from statistical learning showing that humans are adept at detecting differences in the transition probabilities between items in a sequence. We next present recent experiments that directly control for differences in transition probabilities, demonstrating that human behavior also depends critically on the abstract network structure of transitions. Finally, we present computational models that researchers have proposed to explain the effects of network structure on human behavior and cognition. Throughout, we highlight a number of exciting open questions in the study of graph learning that will require creative insights from cognitive scientists and network scientists alike.Comment: 9 pages, 6 figure

Spiritual caregiving silence:an exploration of the phenomenon and its value in end-of-life care

Towards the end of life, silence seems to take increasing prominence in caregiving relationships. A complex phenomenon, silence has been less explored than verbal interventions, yet to be an effective element of care, silence requires skill and practice from professional caregivers. This research, undertaken in the United Kingdom between 2013 and 2016, sought a deeper understanding of a type of silence that contributes to palliative spiritual care. A two phase phenomenological methodology was adopted, using heuristic inquiry and hermeneutic phenomenology. Data were gathered through self-inquiry and unstructured interviews with 15 palliative care chaplains. A descriptive and hermeneutic analysis facilitated explication of the lived experience to produce an interpretation of the nature, meaning and value of spiritual caregiving silence in end-of-life care. Spiritual caregiving silence emerges as a way of being with another person, complementary to speech and non-verbal communication, in which the caregiver takes both an active and participative role. It evokes a sense of companionship and connection and creates accompanied space, allowing the other person to be with themselves in a way they may not be able to be alone; this demands a depth of engagement from the caregiver. Silence provides a means of, and medium for, communication beyond the capacity of words and has the potential to enable change, leading to expression and acknowledgment of truth. It offers patients, and their families, opportunities to find acceptance, restoration and peace. The thesis concludes that spiritual caregiving silence is a person-centred phenomenon that supports the wellbeing of patients at the end of life, and their family members, by drawing on cross-disciplinary knowledge and experience. The interpretive process, illuminated by examples of specialist lived experience, has produced a deeper understanding of the phenomenon that may find resonance with the experience of other caregivers, to stimulate further discussion and inform clinical practice

Structure from noise: Mental errors yield abstract representations of events

Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) model of people's internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources.Comment: 62 pages, 7 figures, 10 table

Information content of note transitions in the music of J. S. Bach

Music has a complex structure that expresses emotion and conveys information. Humans process that information through imperfect cognitive instruments that produce a gestalt, smeared version of reality. How can we quantify the information contained in a piece of music? Further, what is the information inferred by a human, and how does that relate to (and differ from) the true structure of a piece? To tackle these questions quantitatively, we present a framework to study the information conveyed in a musical piece by constructing and analyzing networks formed by notes (nodes) and their transitions (edges). Using this framework, we analyze music composed by J. S. Bach through the lens of network science and information theory. Regarded as one of the greatest composers in the Western music tradition, Bach's work is highly mathematically structured and spans a wide range of compositional forms, such as fugues and choral pieces. Conceptualizing each composition as a network of note transitions, we quantify the information contained in each piece and find that different kinds of compositions can be grouped together according to their information content and network structure. Moreover, we find that the music networks communicate large amounts of information while maintaining small deviations of the inferred network from the true network, suggesting that they are structured for efficient communication of information. We probe the network structures that enable this rapid and efficient communication of information--namely, high heterogeneity and strong clustering. Taken together, our findings shed new light on the information and network properties of Bach's compositions. More generally, our framework serves as a stepping stone for exploring musical complexities, creativity and the structure of information in a range of complex systems.Comment: 22 pages, 13 figure; discussion in section IV and VII expanded, references added, results unchange

Non-equilibrium dynamics and entropy production in the human brain

Living systems operate out of thermodynamic equilibrium at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear whether non-equilibrium dynamics manifest at macroscopic scales, and if so, how such dynamics support higher-order biological functions. Here we present a framework to probe for non-equilibrium dynamics by quantifying entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain fundamentally operates out of equilibrium at large scales. Moreover, we find that the brain produces more entropy -- operating further from equilibrium -- when performing physically and cognitively demanding tasks. By simulating an Ising model, we show that macroscopic non-equilibrium dynamics can arise from asymmetries in the interactions at the microscale. Together, these results suggest that non-equilibrium dynamics are vital for cognition, and provide a general tool for quantifying the non-equilibrium nature of macroscopic systems.Comment: 18 pages, 14 figure