1,074 research outputs found
Is worry different from rumination? Yes, it is more predictive of psychopathology!
No full textObjective: Although worry and rumination are everyday phenomena as well as common symptoms across numerous psychopathological disorders, the theoretical and clinical delineations of both concepts need more clarification. This study explored the degree of overlap between worry and rumination on the levels of standardized questionnaires and a priori lay concepts. Method: The subjective conceptualization of worry and of rumination of 221 undergraduate and graduate students was assessed with the semantic differential technique, together with the frequency and intensity with which they experienced worry and rumination (based on their lay concepts). Standardized self-report measures for worry, rumination, depression, and anxiety were also administered. Results: Worry was viewed as more negative than rumination and was more predictive of anxiety as well as of depression than rumination, especially when the assessment was based on the subjective lay concepts. The different measures of worry and rumination were only moderately correlated with each other. Conclusion: It is concluded that the lay concepts worry and rumination and the hypothetical constructs worry and rumination should not be confused in personality and clinical research.Hintergrund: Die Konzepte "Sorgen" und "Grübeln" bezeichnen gleichermaßen Alltagserleben wie auch verbreitete psychopathologische Symptome. Dennoch bedarf es einer klareren theoretischen und klinischen Abgrenzung zwischen beiden Konzepten. In der vorliegenden Studie untersuchten wir, inwieweit sich Symptome des Sich-Sorgens und des Grübelns überlappen, wenn sie einerseits mittels standardisierter Fragebögen, andererseits auf der Basis von Häufigkeitsangaben, die auf a-priori gegebene Laienkonzepte der Probanden zurückgingen, erfasst wurden. Methoden: Die subjektiven Konzepte von "Sorgen" und "Grübeln" von 221 deutschsprachigen Studenten wurden mit dem Semantischen Differential untersucht, ferner die Häufigkeit und Intensität, mit der Sorgen und Grübeln erlebt wurden. Standardisierte Fragebögen für Sorgen, Grübeln, Depression und Angst wurden ebenfalls eingesetzt. Ergebnisse: Sich-Sorgen wurden als negativer eingeschätzt als Grübeln und prädizierte sowohl Angst- als auch Depressionssymptome besser als Grübeln. Dies galt besonders dann, wenn den Häufigkeitsangaben die Laienkonzepte zugrunde lagen. Die verschiedenen Maße für Sorgen und Grübeln waren nur mäßig miteinander korreliert. Schlussfolgerung: Unsere Schlussfolgerung ist, dass die Laienkonzepte "Sich-Sorgen" und "Grübeln" und die entsprechenden hypothetischen Konstrukte in der Persönlichkeits- und klinischen Forschung nicht miteinander vermischt werden sollten
Linda Grace Hoyer Updike: Woman, Author, and Mother
No full textLinda Grace Hoyer was a brilliant individual. She graduated from Ursinus College at the age of nineteen, received a master\u27s from Cornell University, and after many years of diligent work, published two novels and a myriad of short stories. She lived an unusual life: reflective, feminine in her thought processes, but nevertheless somewhat stubborn in a time when women were meant to fill a subordinate role. I have found through my research that Hoyer\u27s brilliance did not lie in her intellect and writing alone. In fact, as demonstrated by her literature\u27s autobiographical nature, her brilliance as a writer seemed to stem from her unique ability as a human being and mother. Through short stories and letters that were never published, as well as through an interview with her son, John Updike, I found that she had a very deep capacity to love. This love manifested itself in a strong bond with her native Pennsylvania, in her forty-some cats, and in her son. She loved the farm where she was born (and later died) despite the years she spent there utterly alone. She loved her son despite his fame and prestige; fame and prestige she never experienced as a writer. She loved to write, despite bearing seemingly endless rejections. It was her love, as a woman and mother, which emanated from her writing, and made her the brilliant author that she was
sj-docx-1-cpx-10.1177_21677026221101379 – Supplemental material for Change of Threat Expectancy as Mechanism of Exposure-Based Psychotherapy for Anxiety Disorders: Evidence From 8,484 Exposure Exercises of 605 Patients
No full textSupplemental material, sj-docx-1-cpx-10.1177_21677026221101379 for Change of Threat Expectancy as Mechanism of Exposure-Based Psychotherapy for Anxiety Disorders: Evidence From 8,484 Exposure Exercises of 605 Patients by Andre Pittig, Ingmar Heinig, Stephan Goerigk, Jan Richter, Maike Hollandt, Ulrike Lueken, Paul Pauli, Jürgen Deckert, Tilo Kircher, Benjamin Straube, Peter Neudeck, Katja Koelkebeck, Udo Dannlowski, Volker Arolt, Thomas Fydrich, Lydia Fehm, Andreas Ströhle, Christina Totzeck, Jürgen Margraf, Silvia Schneider, Jürgen Hoyer, Winfried Rief, Michelle G. Craske, Alfons O. Hamm and Hans-Ulrich Wittchen in Clinical Psychological Science</p
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No full textAuthor(s): Hoyer, Daniel; Reddish, Jenny; Turchin, Pete
The picture of David Hoyer from Jan Kupecký at Leipzig
No full textThe thesis focuses on portrait on painting of David Hoyer from baroque artist Jan Kupecky that is placed in Museum der bildenden Künste in Leipzig. Firstly, I will deal with life and work of the author Jan Kupecky. The main focus will be dedicated to the painting from 1711 which depicts Saxon painter David Hoyer with lyra. I will try to unveil more information about life of the depicted man, consequently, I will concentrate on the actual type of portaval style and mention other significant work of the same nature made by the author prior 1711. This actual painting example will allow me to show how the portrait creation was forming not only in life of Jan Kupecky but also in context of whole Europe
On the polynomial integrability of the Hoyer systems
No full textAgraïments: The second author is supported by FCT through the project PTDC/MAT/117106/2010 and through CAMGSD, Lisbon.The Hoyer polynomial differential systems depend on nine parameters. We provide necessary conditions in order that these systems have two functionally independent polynomial first integrals. We show that these conditions are not sufficient. Additionally, we illustrate how can be computed the polynomial first integrals of these systems using the Kovalevsky exponents
On the polynomial integrability of the Hoyer systems
No full textAgraïments: The second author is supported by FCT through the project PTDC/MAT/117106/2010 and through CAMGSD, Lisbon.The Hoyer polynomial differential systems depend on nine parameters. We provide necessary conditions in order that these systems have two functionally independent polynomial first integrals. We show that these conditions are not sufficient. Additionally, we illustrate how can be computed the polynomial first integrals of these systems using the Kovalevsky exponents
WIFA-k: Ein neues Messinstrument zur zeitökonomischen Erfassung allgemeiner Wirkfaktoren nach Grawe
No full textResearch on common and differential factors in the therapeutic process is impeded by the lack of instruments suitable for assessing common change mechanisms. This study presents the psychometric properties of a newly developed time-economic instrument (WIFA-k), which was designed to assess common factors of psychotherapy as designed by Grawe. Within a multi-center study comparing the efficacy of cognitive therapy and psychodynamic therapy in the treatment of social phobia, 6 raters assessed 25 randomly selected, videotaped therapy sessions of each treatment approach, and evaluated common factors using the Wifa-k. Interrater-reliability was found to be high for the items "resource activation", "motivational clarification" and "mastery" and low for the items "therapeutic relationship" and "problem activation". Ways to increase reliability and validity of the scale are discussed
xarray: v0.8.0
No full text<a class="anchor" href="#v080-2-august-2016"><span class="octicon octicon-link"></span></a>v0.8.0 (2 August 2016)
<p>This release includes new features and bug fixes, including several breaking changes.</p>
<a class="anchor" href="#breaking-changes"><span class="octicon octicon-link"></span></a>Breaking changes
<ul>
<li>Dropped support for Python 2.6 (#855).</li>
<li>Indexing on multi-index now drop levels, which is consistent with pandas. It also changes the name of the dimension / coordinate when the multi-index is reduced to a single index (#802).</li>
<li>Contour plots no longer add a colorbar per default (#866). Filled contour plots are unchanged.</li>
<li>
DataArray.values and .data now always returns an NumPy array-like object, even for 0-dimensional arrays with object dtype (#867). Previously, .values returned native Python objects in such cases. To convert the values of scalar arrays to Python objects, use the .item() method.</li>
</ul>
<a class="anchor" href="#enhancements"><span class="octicon octicon-link"></span></a>Enhancements
<ul>
<li>Groupby operations now support grouping over multidimensional variables. A new method called xarray.Dataset.groupby_bins has also been added to allow users to specify bins for grouping. The new features are described in groupby.multidim and examples.multidim. By <a href="http://github.com/rabernat">Ryan Abernathey</a>.</li>
<li>DataArray and Dataset method where now supports a drop=True option that clips coordinate elements that are fully masked. By <a href="https://github.com/pwolfram">Phillip J. Wolfram</a>.</li>
<li>New top level merge function allows for combining variables from any number of Dataset and/or DataArray variables. See merge for more details. By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>DataArray and Dataset method resample now supports the keep_attrs=False option that determines whether variable and dataset attributes are retained in the resampled object. By <a href="https://github.com/mcgibbon">Jeremy McGibbon</a>.</li>
<li>Better multi-index support in DataArray and Dataset sel and loc methods, which now behave more closely to pandas and which also accept dictionaries for indexing based on given level names and labels (see multi-level indexing). By <a href="https://github.com/benbovy">Benoit Bovy</a>.</li>
<li>New (experimental) decorators xarray.register_dataset_accessor and xarray.register_dataarray_accessor for registering custom xarray extensions without subclassing. They are described in the new documentation page on internals. By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>Round trip boolean datatypes. Previously, writing boolean datatypes to netCDF formats would raise an error since netCDF does not have a bool datatype. This feature reads/writes a dtype attribute to boolean variables in netCDF files. By <a href="https://github.com/jhamman">Joe Hamman</a>.</li>
<li>2D plotting methods now have two new keywords (cbar_ax and cbar_kwargs), allowing more control on the colorbar (#872). By <a href="https://github.com/fmaussion">Fabien Maussion</a>.</li>
<li>New Dataset method filter_by_attrs, akin to netCDF4.Dataset.get_variables_by_attributes, to easily filter data variables using its attributes. <a href="https://github.com/ocefpaf">Filipe Fernandes</a>.</li>
</ul>
<a class="anchor" href="#bug-fixes"><span class="octicon octicon-link"></span></a>Bug fixes
<ul>
<li>Attributes were being retained by default for some resampling operations when they should not. With the keep_attrs=False option, they will no longer be retained by default. This may be backwards-incompatible with some scripts, but the attributes may be kept by adding the keep_attrs=True option. By <a href="https://github.com/mcgibbon">Jeremy McGibbon</a>.</li>
<li>Concatenating xarray objects along an axis with a MultiIndex or PeriodIndex preserves the nature of the index (#875). By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>Fixed bug in arithmetic operations on DataArray objects whose dimensions are numpy structured arrays or recarrays #861, #837. By <a href="https://github.com/maciekswat">Maciek Swat</a>.</li>
<li>
decode_cf_timedelta now accepts arrays with ndim >1 (#842). This fixes issue #665. <a href="https://github.com/ocefpaf">Filipe Fernandes</a>.</li>
<li>Fix a bug where xarray.ufuncs that take two arguments would incorrectly use to numpy functions instead of dask.array functions (#876). By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>Support for pickling functions from xarray.ufuncs (#901). By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>
Variable.copy(deep=True) no longer converts MultiIndex into a base Index (#769). By <a href="https://github.com/benbovy">Benoit Bovy</a>.</li>
<li>Fixes for groupby on dimensions with a multi-index (#867). By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>Fix printing datasets with unicode attributes on Python 2 (#892). By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>Fixed incorrect test for dask version (#891). By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>Fixed dim argument for isel_points/sel_points when a pandas.Index is passed. By <a href="https://github.com/shoyer">Stephan Hoyer</a>.</li>
<li>
xarray.plot.contour now plots the correct number of contours (#866). By <a href="https://github.com/fmaussion">Fabien Maussion</a>.</li>
</ul>
Baseline Patient Characteristics Predicting Outcome and Attrition in Cognitive Therapy for Social Phobia: Results from a Large Multicentre Trial
No full textWe examined the role of baseline patient characteristics as predictors of outcome (end-state functioning, response and remission) and attrition for cognitive therapy (CT) in social anxiety disorder (SAD). Beyond socio-demographic and clinical variables such as symptom severity and comorbidity status, previously neglected patient characteristics (e.g., personality, self-esteem, shame, interpersonal problems and attachment style) were analysed. MethodData came from the CT arm of a multicentre RCT with n=244 patients having DSM-IV SAD. CT was conducted according to the manual by Clark and Wells. Severity of SAD was assessed at baseline and end of treatment with the Liebowitz Social Anxiety Scale (LSAS). Multiple linear regression analyses and logistic regression analyses were applied. ResultsUp to 37% of the post-treatment variance (LSAS) could be explained by all pre-treatment variables combined. Symptom severity (baseline LSAS) was consistently negatively associated with end-state functioning and remission, but not with response. Number of comorbid diagnoses was negatively associated with end-state functioning and response, but not with remission. Self-esteem was positively associated with higher end-state functioning and more shame with better response. Attrition could not be significantly predicted. ConclusionsThe results indicate that the initial probability for treatment success mainly depends on severity of disorder and comorbid conditions while other psychological variables are of minor importance, at least on a nomothetic level. This stands in contrast with efforts to arrive at an empirical-based foundation for differential indication and argues to search for more potent moderators of therapeutic change rather on the process level. Copyright (c) 2014 John Wiley & Sons, Ltd.German Ministry of Education and Research (BMBF) [01GV0607
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