3,650 research outputs found
Understanding the therapeutic alliance between nurses and consumers with anorexia nervosa in the context of the inpatient setting : a mixed methods study
University of Technology Sydney. Faculty of Health.Introduction and Aims: The evidence informing the treatment of anorexia nervosa is limited. It is established that consumers with AN value professional interpersonal relationships with nurses, finding these relationships meaningful and therapeutic. A therapeutic alliance is associated with enhanced outcomes, and may be a promising aspect of the treatment for AN. However, therapeutic alliance is not well understood in the context of the inpatient setting. The aim of this research was to establish a greater understanding of the nature of the therapeutic alliance between nurses and consumers with anorexia nervosa, within the context of the inpatient setting.
Method: This study employed a mixed methods approach, two phase explanatory sequential design. The initial phase consisted of a quantitative investigation, involving both consumers and nurses in surveys. Surveys measured the perceived degree of alliance, and investigated other elements of ward context. The subsequent qualitative phase involved semi-structured interviews with both nurses and consumers. Interviews were focused on the relationships between nurses and consumers and the implications of ward context on those relationships. Interviews also developed a qualitative understanding for interpreting the quantitative findings.
Results: Consumers reported a relatively low perceived alliance with nurses, a relatively low perceived satisfaction with care, and a severe degree of eating disorder psychopathology. Nurses reported good attitudes towards consumers with acute mental illness. Consumers and nurses had a diverging perception of the alliance, as nurses perceived a higher strength of alliance compared to consumers. The interviews revealed that the alliance was valued and had positive environmental implications. However, anorexia nervosa as an illness was detrimental to the relationships between nurses and consumers. In developing a therapeutic alliance, nurses and consumers actively separated from the destructive implications of anorexia nervosa. Nurses’ use of authority was influential over the development of the alliance. Multiple contextual factors within the ward influenced the therapeutic alliance between nurses and consumers.
Discussion and Conclusion: The way that nurses utilised their position of power determined the quality of the therapeutic alliance. A successful therapeutic separation and mutuality was dependent on confidence in the understanding that the orientation of power was employed to credit and protect the consumer. A balance of ‘love and limits’ developed a therapeutic separation, which preceded the mutuality of a therapeutic alliance. Contextual factors within the inpatient setting can be modified to enhance the capacity for nurses to develop therapeutic alliances with consumers
Galaxies in LCDM with Halo Abundance Matching: luminosity-velocity relation, baryonic mass-velocity relation, velocity function and clustering
It has long been regarded as difficult for a cosmological model to account
simultaneously for the galaxy luminosity, mass, and velocity distributions. We
revisit this issue using a modern compilation of observational data along with
the best available large-scale cosmological simulation of dark matter. We find
that the standard cosmological model, used in conjunction with halo abundance
matching (HAM) and simple dynamical corrections, fits all basic statistics of
galaxies with circular velocities Vcirc > 80 km/s. Our observational constraint
is the luminosity-velocity relation which allows all types of galaxies to be
included. We have compiled data for a variety of galaxies ranging from dwarf
irregulars to giant ellipticals. The data present a clear monotonic
luminosity-velocity relation from 50 km/s to 500 km/s, with a bend below 80
km/s and a systematic offset between late- and early-type galaxies. For
comparison to theory, we employ our LCDM "Bolshoi" simulation of dark matter,
which has unprecedented mass and force resolution. We use halo abundance
matching to assign rank-ordered galaxy luminosities to the dark matter halos.
The resulting predictions for the luminosity-velocity relation are in excellent
agreement with the available data on both early-type and late-type galaxies for
the luminosity range from Mr = -14-22. We also compare our predictions for the
"cold" baryon mass (i.e., stars and cold gas) of galaxies as a function of
circular velocity with the available observations, again finding a very good
agreement. The predicted circular velocity function is in agreement with the
galaxy velocity function for 80-400 km/s. However, we find that the dark matter
halos with Vcirc < 80 km/s are much more abundant than observed galaxies with
the same Vcirc . We find that the two-point correlation function of galaxies in
our model matches very well the results from the SDSS.Comment: 40 pages, 18 figures, published in Ap
Simulating the Large-Scale Structure of HI Intensity Maps
Intensity mapping of neutral hydrogen (HI) is a promising observational probe
of cosmology and large-scale structure. We present wide field simulations of HI
intensity maps based on N-body simulations of a box with
particles (particle mass ).
Using a conditional mass function to populate the simulated dark matter density
field with halos below the mass resolution of the simulation (), we assign HI to
those halos according to a phenomenological halo to HI mass relation. The
simulations span a redshift range of 0.35 < z < 0.9 in redshift bins of width
and cover a quarter of the sky at an angular resolution
of about 7'. We use the simulated intensity maps to study the impact of
non-linear effects and redshift space distortions on the angular clustering of
HI. Focusing on the autocorrelations of the maps, we apply and compare several
estimators for the angular power spectrum and its covariance. We verify that
these estimators agree with analytic predictions on large scales and study the
validity of approximations based on Gaussian random fields, particularly in the
context of the covariance. We discuss how our results and the simulated maps
can be useful for planning and interpreting future HI intensity mapping
surveys.Comment: 35 pages, 19 Figures. Accepted for publication in JCA
Low-mass galaxy assembly in simulations: regulation of early star formation by radiation from massive stars
Despite recent success in forming realistic present-day galaxies, simulations
still form the bulk of their stars earlier than observations indicate. We
investigate the process of stellar mass assembly in low-mass field galaxies, a
dwarf and a typical spiral, focusing on the effects of radiation from young
stellar clusters on the star formation (SF) histories. We implement a novel
model of SF with a deterministic low efficiency per free-fall time, as observed
in molecular clouds. Stellar feedback is based on observations of star-forming
regions, and includes radiation pressure from massive stars, photoheating in H
II regions, supernovae and stellar winds. We find that stellar radiation has a
strong effect on the formation of low-mass galaxies, especially at z > 1, where
it efficiently suppresses SF by dispersing cold and dense gas, preventing
runaway growth of the stellar component. This behaviour is evident in a variety
of observations but had so far eluded analytical and numerical models without
radiation feedback. Compared to supernovae alone, radiation feedback reduces
the SF rate by a factor of ~100 at z < 2, yielding rising SF histories which
reproduce recent observations of Local Group dwarfs. Stellar radiation also
produces bulgeless spiral galaxies and may be responsible for excess thickening
of the stellar disc. The galaxies also feature rotation curves and baryon
fractions in excellent agreement with current data. Lastly, the dwarf galaxy
shows a very slow reduction of the central dark matter density caused by
radiation feedback over the last ~7 Gyr of cosmic evolution
An Integrated System at the Bleien Observatory for Mapping the Galaxy
We describe the design and performance of the hardware system at the Bleien
Observatory. The system is designed to deliver a map of the Galaxy for studying
the foreground contamination of low-redshift (z=0.13--0.43) H
intensity mapping experiments as well as other astronomical Galactic studies.
This hardware system is composed of a 7m parabolic dish, a dual-polarization
corrugated horn feed, a pseudo correlation receiver, a Fast Fourier Transform
spectrometer, and an integrated control system that controls and monitors the
progress of the data collection. The main innovative designs in the hardware
are (1) the pseudo correlation receiver and the cold reference source within
(2) the high dynamic range, high frequency resolution spectrometer and (3) the
phase-switch implementation of the system. This is the first time these
technologies are used together for a L-band radio telescope to achieve an
electronically stable system, which is an essential first step for wide-field
cosmological measurements. This work demonstrates the prospects and challenges
for future H intensity mapping experiments.Comment: 11 pages, 12 figures, 1 table, Submitted to MNRA
Amortised likelihood-free inference for expensive time-series simulators with signatured ratio estimation
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel algorithms for estimating otherwise intractable likelihood functions using a likelihood ratio trick based on binary classifiers. Consequently, efficient likelihood approximations can be obtained whenever good probabilistic classifiers can be constructed. We propose a kernel classifier for sequential data using path signatures based on the recently introduced signature kernel. We demonstrate that the representative power of signatures yields a highly performant classifier, even in the crucially important case where sample numbers are low. In such scenarios, our approach can outperform sophisticated neural networks for common posterior inference tasks
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