Cultural Differences in Group Therapy: A Phenomenological Study of the Lived Embodied Experience of the Cultural Bump

Utilizing transcendental phenomenology, this study sought to better understand dance/movement therapists’ experiences of the culture bump phenomenon in the group therapy setting. Culture bumps are defined as moments in which two or more people enter a situation with different culturally-based expectations about customs, behavior, beliefs, communication styles, and other norms (Archer & Nickson, 2012). Data were collected using individual inperson semi-structured interviews with five Chicagoland dance/movement therapists who self identified as having experienced the phenomenon of the culture bump while in the group therapy setting. Data analysis was completed using Moustakas’ (1994) adaptation of the Stevick-Colaizzi-Keen method and resulted in five textural-structural themes that describe the experience of the phenomenon of the culture bump: a) elusory and complex in nature, b) at its essence, about a meeting of differing expectations, c) having a shifting/changing quality to it, d) inextricably tied to the participant’s own cultural context, and e) therapeutically important material. The participants’ experiences indicated culture bumps are a common occurrence in the group dance/movement therapy setting, and both their presence and the processing of them are breeding grounds for necessary conversations about cultural difference. 54 pages - submitted as an article to the American Journal of Dance Therapy in February of 2018 in a format that meets the criteria for that publication, and so is shorter than a standard thesis

Building a semantically annotated corpus of clinical texts

In this paper, we describe the construction of a semantically annotated corpus of clinical texts for use in the development and evaluation of systems for automatically extracting clinically significant information from the textual component of patient records. The paper details the sampling of textual material from a collection of 20,000 cancer patient records, the development of a semantic annotation scheme, the annotation methodology, the distribution of annotations in the final corpus, and the use of the corpus for development of an adaptive information extraction system. The resulting corpus is the most richly semantically annotated resource for clinical text processing built to date, whose value has been demonstrated through its use in developing an effective information extraction system. The detailed presentation of our corpus construction and annotation methodology will be of value to others seeking to build high-quality semantically annotated corpora in biomedical domains

Integrating Intersectional Identity into Clinical Supervision

Differentiated from general social work supervision, clinical supervision is a core means by which post-graduate clinical social workers develop and refine their professional skills and ethical practice, and secure terminal licensure. The integration of the supervisee’s composite intersecting aspects of identity, which is conceptualized here as their intersectional identity, is a critical component of clinical supervision given the ethical demands of the profession, the nature of growth and regrowth that occurs in any educational process, and the impact each clinical social worker’s self has on their own clinical practice (Association of Social Work Boards, 2013; Bubar, Cespedes, & Bundy-Fazioli, 2016; Kolb, 1984). The structure and relationship of clinical supervision has a significant role in supporting supervisees as they begin to incorporate aspects of their intersectional identities with their clinical social work practice. This dissertation offers recommendations from the existing body of literature and the results of an exploratory qualitative study on how themes and concepts from intersectionality and intersectional identity might be integrated into clinical supervision

Problematic Interactions between AI and Health Privacy

Problematic Interactions Between AI and Health Privacy Nicholson Price, University of Michigan Law SchoolFollow Abstract The interaction of artificial intelligence (AI) and health privacy is a two-way street. Both directions are problematic. This Essay makes two main points. First, the advent of artificial intelligence weakens the legal protections for health privacy by rendering deidentification less reliable and by inferring health information from unprotected data sources. Second, the legal rules that protect health privacy nonetheless detrimentally impact the development of AI used in the health system by introducing multiple sources of bias: collection and sharing of data by a small set of entities, the process of data collection while following privacy rules, and the use of non-health data to infer health information. The result is an unfortunate anti-synergy: privacy protections are weak and illusory, but rules meant to protect privacy hinder other socially valuable goals. The state of affairs creates biases in health AI, privileges commercial research over academic research, and is ill-suited to either improve health care or protect patients. The health system deeply needs a new bargain between patients and the health system about the uses of patient data

Automatic de-identification of textual documents in the electronic health record: a review of recent research

<p>Abstract</p> <p>Background</p> <p>In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here.</p> <p>Methods</p> <p>This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers.</p> <p>Results</p> <p>The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries.</p> <p>Conclusions</p> <p>In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.</p

Problematic Interactions between AI and Health Privacy

The interaction of artificial intelligence (“AI”) and health privacy is a two-way street. Both directions are problematic. This Article makes two main points. First, the advent of artificial intelligence weakens the legal protections for health privacy by rendering deidentification less reliable and by inferring health information from unprotected data sources. Second, the legal rules that protect health privacy nonetheless detrimentally impact the development of AI used in the health system by introducing multiple sources of bias: collection and sharing of data by a small set of entities, the process of data collection while following privacy rules, and the use of non-health data to infer health information. The result is an unfortunate anti- synergy: privacy protections are weak and illusory, but rules meant to protect privacy hinder other socially valuable goals. This state of affairs creates biases in health AI, privileges commercial research over academic research, and is ill-suited to either improve health care or protect patients’ privacy. The ongoing dysfunction calls for a new bargain between patients and the health system about the uses of patient data

How reliable are case formulations?: a systematic literature review

Objectives: This systematic literature review investigated the inter-rater and test–retest reliability of case formulations. We considered the reliability of case formulations across a range of theoretical modalities and the general quality of the primary research studies. Methods: A systematic search of five electronic databases was conducted in addition to reference list trawling to find studies that assessed the reliability of case formulation. This yielded 18 studies for review. A methodological quality assessment tool was developed to assess the quality of studies, which informed interpretation of the findings. Results: Results indicated inter-rater reliability mainly ranging from slight (.1–.4) to substantial (.81–1.0). Some studies highlighted that training and increased experience led to higher levels of agreement. In general, psychodynamic formulations appeared to generate somewhat increased levels of reliability than cognitive or behavioural formulations; however, these studies also included methods that may have served to inflate reliability, for example, pooling the scores of judges. Only one study investigated the test–retest reliability of case formulations yielding support for the stability of formulations over a 3-month period. Conclusions: Reliability of case formulations is varied across a range of theoretical modalities, but can be improved; however, further research is required to strengthen our conclusions

Personal information privacy: what's next?

In recent events, user-privacy has been a main focus for all technological and data-holding companies, due to the global interest in protecting personal information. Regulations like the General Data Protection Regulation (GDPR) set firm laws and penalties around the handling and misuse of user data. These privacy rules apply regardless of the data structure, whether it being structured or unstructured. In this work, we perform a summary of the available algorithms for providing privacy in structured data, and analyze the popular tools that handle privacy in textual data; namely medical data. We found that although these tools provide adequate results in terms of de-identifying medical records by removing personal identifyers (HIPAA PHI), they fall short in terms of being generalizable to satisfy nonmedical fields. In addition, the metrics used to measure the performance of these privacy algorithms don't take into account the differences in significance that every identifier has. Finally, we propose the concept of a domain-independent adaptable system that learns the significance of terms in a given text, in terms of person identifiability and text utility, and is then able to provide metrics to help find a balance between user privacy and data usability

THIRD CULTURE KIDS (TCKs) GO TO COLLEGE: A RETROSPECTIVE NARRATIVE INQUIRY OF INTERNATIONAL UPBRINGING AND COLLEGIATE ENGAGEMENT

BACKGROUND: Third Culture Kids (TCKs) are those who have been raised in a culture outside of the culture of their parents, usually in a host country that differs from the country of their birth, because of their parents’ work or religious endeavors. Some of the groups that identify themselves as TCKs include children of military service members stationed overseas, children of members of the Foreign Service, and the children of missionaries. These children are growing up in a culture and society that is different from their parents’ passport country and may vastly differ in language spoken, religious beliefs, and cultural norms. Pollock and Van Reken (2001) explain TCKs as being between cultures, stating that the third culture is developed by the child to explain an identity that is different from that of the host country or the parents’ home country. This retrospective narrative inquiry explored the undergraduate college experiences of Adult Third Culture Kids (ATCKs) to understand the risk and protective factors associated with repatriation and collegiate engagement. METHODS: This study employed a qualitative approach combining heuristic analysis and procedures of grounded theory during data collection, analysis, and interpretation of findings. Twelve semi-structured interviews were conducted face-to-face with individuals who self-identified as ATCKs and had completed a four year undergraduate program earning a degree. RESULTS: Concepts related to understanding the self, and meaningful connections and relationships emerged from the data revealing how repatriation can be simultaneously volatile and emotionally grounding. Themes uncovered during data analysis included perceptions of self-identity, investment, the concept of home, uneven development, and factors contributing to college choice. DISCUSSION: Research findings suggest the need for culturally informed administrative practices to mitigate psychosocial challenges associated with academic engagement. Interventions related to student identification procedures, supportive resources, and campus life programs should be incorporated to support multicultural students starting at the time of application and continuing through to graduation

Strategies to Mitigate Nurse Turnover in Eastern and Northern Virginia

Registered nurses leaving the workplace are estimated to cost healthcare organizations and society between $1.4 and$2.1 billion annually. The purpose of this multiple case study was to explore what strategies leaders of healthcare organizations from the Eastern and Northern regions of Virginia can use to mitigate the effects of nurse turnover and its cost to the organization. The target population consisted of 8 RNs who experienced turnover during their nursing careers. The conceptual framework for this study was Herzberg\u27s dual-factor theory. Face-to-face semistructured interviews were conducted and publically available documents were garnered. Thematic reduction of participants\u27 interviews, coupled with data triangulation between narratives and publically available documents resulted in the emergence of 4 common themes: immediate nurse supervisor training, staff support within departments, nurse pay commensurate with the time demands, and education requirements. All participants cited burnout, stress, and career development as reasons for seeking new employment, and the topics of pay and staffing had high frequencies of occurrence. The RNs interviewed expressed nurses have different sources of satisfaction and these sources affect motivation and intent to leave. Social implications include providing insights into conditions that could strengthen the healthcare workplace environment and contribute to patient care improvements, reduce turnover costs, and increased productivity. Improved retention could also result in greater stability of the RN workforce in health care organizations