A phenomenological investigation of licensed professional counselors' perspectives of clinical intuition

The purpose of this study was to investigate the essence of the experience of clinical intuition through the perspective of licensed professional counselors. Despite the attention that intuition had been given in other professional fields, a lack of research that was specific to the counseling profession existed on this topic. This dearth of literature existed despite the apparent connection between counselor development and intuition. For instance, models of counselor development depicted how counselors increase their awareness of themselves, their clients, and the counseling relationship as they gain more clinical experience, while theories on the nature of intuition suggested that experience and awareness produce intuitive knowledge. In spite of that association, clinical intuition in the field of licensed professional counselors had not been examined. Given that counselors’ clinical intuition was little understood, a phenomenological design was selected for this investigation. This type of qualitative study provided a way to discover the core essential meanings of clinical intuition. Furthermore, it created the foundation for future studies in this area. The participants were comprised of nine licensed professional counselors in the state of North Carolina and all met the criteria for this study. Their experience ranged from 5 years to 36 years. These counselors worked in diverse settings and had acquired various kinds of postgraduate training. Interviews were conducted in the offices where counselors usually work with clients. Transcriptions of those interviews yielded the data that were analyzed and synthesized based on Moustakas’ (1994) phenomenological method. That process involved the Epoche, phenomenological reduction, imaginative variation, and synthesis. The data revealed six core themes: (1) unconscious associations; (2) conscious associations; (3) moments preceding the arrival of intuitive knowledge; (4) initial appearance; (5) manifestation of intuitive knowledge; and (6) the nature of the intuitive information. Each of the six themes was composed of clusters. Within the first theme of unconscious associations, participants made inferences about clinical knowledge and countertransference reactions that had occurred outside of their conscious awareness. The second theme of conscious associations contained counselors’ attention to their identification and resonance with clients as well as their countertransference to clients. This theme also included awareness of clinical knowledge and clients’ nonverbal and verbal communication. The third theme concerned the accepting, present, and expectant qualities that preceded the arrival of the intuitive knowledge. The fourth theme captured the holistic, immediate, certain, and sacred characteristics that seemed to imbue the presentation of the intuitive information. The fifth theme captured the way clinical intuition manifested in counselors, and it seemed to conform to the sixth theme which described the degree and quality of that information. Clinical intuition appeared to be a slow development of increasing levels of unconscious and conscious associations. In a state of alert receptivity, something in the clinical situation seemed to catalyze those developing connections. Counselors experienced that moment as a felt sense, gut feeling, recognition of a pattern, or symbolic representation. The manner in which clinical intuition arrived seemed to correspond with the degree of consciousness and the amount of affective and cognitive material contained in the knowledge. These findings were reviewed in relation to the relevant literature on intuition. The implications of this study to the field of counseling were also offered. Furthermore, suggestions for future studies on this topic were provided

Solid-State Dynamic Nuclear Polarization at 263 GHz: Spectrometer Design and Experimental Results

Dynamic Nuclear Polarization (DNP) experiments transfer polarization from electron spins to nuclear spins with microwave irradiation of the electron spins for enhanced sensitivity in nuclear magnetic resonance (NMR) spectroscopy. Design and testing of a spectrometer for magic angle spinning (MAS) DNP experiments at 263 GHz microwave frequency, 400 MHz 1H frequency is described. Microwaves are generated by a novel continuous-wave gyrotron, transmitted to the NMR probe via a transmission line, and irradiated on a 3.2 mm rotor for MAS DNP experiments. DNP signal enhancements of up to 80 have been measured at 95 K on urea and proline in water–glycerol with the biradical polarizing agent TOTAPOL. We characterize the experimental parameters affecting the DNP efficiency: the magnetic field dependence, temperature dependence and polarization build-up times, microwave power dependence, sample heating effects, and spinning frequency dependence of the DNP signal enhancement. Stable system operation, including DNP performance, is also demonstrated over a 36 h period.National Institutes of Health (U.S.) (NIH grant EB-002804)National Institutes of Health (U.S.) (NIH grant EB-002026

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

DogmatiX Tracks down Duplicates in XML

Duplicate detection is the problem of detecting different entries in a data source representing the same real-world entity. While research abounds in the realm of duplicate detection in relational data, there is yet little work for duplicates in other, more complex data models, such as XML. In this paper, we present a generalized framework for duplicate detection, dividing the problem into three components: candidate definition defining which objects are to be compared, duplicate definition defining when two duplicate candidates are in fact duplicates, and duplicate detection specifying how to efficiently find those duplicates. Using this framework, we propose an XML duplicate detection method, DogmatiX, which compares XML elements based not only on their direct data values, but also on the similarity of their parents, children, structure, etc. We propose heuristics to determine which of these to choose, as well as a similarity measure specifically geared towards the XML data model. An evaluation of our algorithm using several heuristics validates our approach.Peer Reviewe

Relationship-Based Duplicate Detection

Recent work both in the relational and the XML world have shown that the efficacy and efficiency of duplicate detection is enhanced by regarding relationships between ancestors and descendants. We present a novel comparison strategy that uses relationships but disposes of the strict bottom-up and topdown approaches proposed for hierarchical data. Instead, pairs of objects at any level of the hierarchy are compared in an order that depends on their relationships: Objects with many dependants influence many other duplicity-decisions and thus it should be decided early if they are duplicates themselves. We apply this ordering strategy to two algorithms. RECONA allows to re-examine an object if its influencing neighbors turn out to be duplicates. Here ordering reduces the number of such re-comparisons. ADAMA is more efficient by not allowing any re-comparison. Here the order minimizes the number of mistakes made

DogmatiX Tracks down Duplicates in XML

Duplicate detection is the problem of detecting di#erent entries in a data source representing the same real-world entity. While research abounds in the realm of duplicate detection in relational data, there is yet little work for duplicates in other, more complex data models, such as XML. In this paper, we present a generalized framework for duplicate detection, dividing the problem into three components: candidate definition defining which objects are to be compared, duplicate definition defining when two duplicate candidates are in fact duplicates, and duplicate detection specifying how to e#ciently find those duplicates

Fuzzy Duplicate Detection on XML Data

XML is popular for data exchange and data publishing on the Web, but it comes with errors and inconsistencies inherent to real-world data. Hence, there is a need for XML data cleansing, which requires solutions for fuzzy duplicate detection in XML. The hierarchical and semi-structured nature of XML strongly differs from the flat and structured relational model, which has received the main attention in duplicate detection so far. We consider four major challenges of XML duplicate detection to develop effective, efficient, and scalable solutions to the problem