1,921 research outputs found
Should I stay or should I go? Exploring the job preferences of allied health professionals working with people with disability in rural Australia
Introduction: The uneven distribution of allied health professionals (AHPs) in rural and remote Australia and other countries is well documented. In Australia, like elsewhere, service delivery to rural and remote communities is complicated because relatively small numbers of clients are dispersed over large geographic areas. This uneven distribution of AHPs impacts significantly on the provision of services particularly in areas of special need such as mental health, aged care and disability services.
Objective: This study aimed to determine the relative importance that AHPs (physiotherapists, occupational therapists, speech pathologists and psychologists â âtherapistsâ) living in a rural area of Australia and working with people with disability, place on different job characteristics and how these may affect their retention.
Methods: A cross-sectional survey was conducted using an online questionnaire distributed to AHPs working with people with disability in a rural area of Australia over a 3-month period. Information was sought about various aspects of the AHPsâ current job, and their workforce preferences were explored using a bestâworst scaling discrete choice experiment (BWSDCE). Conditional logistic and latent class regression models were used to determine AHPsâ relative preferences for six different job attributes.
Results: One hundred ninety-nine AHPs completed the survey; response rate was 51 %. Of those, 165 completed the BWSDCE task. For this group of AHPs, âhigh autonomy of practiceâ is the most valued attribute level, followed by âtravel BWSDCE arrangements: one or less nights away per monthâ, âtravel arrangements: two or three nights away per monthâ and âadequate access to professional developmentâ. On the other hand, the least valued attribute levels were âtravel arrangements: four or more nights per monthâ, âlimited autonomy of practiceâ and âminimal access to professional developmentâ. Except for âsome job flexibilityâ, all other attributes had a statistical influence on AHPsâ job preference. Preferences differed according to age, marital status and having dependent children.
Conclusions: This study allowed the identification of factors that contribute to AHPsâ employment decisions about staying and working in a rural area. This information can improve job designs in rural areas to increase retention
A Bayesian approach to the follow-up of candidate gravitational wave signals
Ground-based gravitational wave laser interferometers (LIGO, GEO-600, Virgo
and Tama-300) have now reached high sensitivity and duty cycle. We present a
Bayesian evidence-based approach to the search for gravitational waves, in
particular aimed at the followup of candidate events generated by the analysis
pipeline. We introduce and demonstrate an efficient method to compute the
evidence and odds ratio between different models, and illustrate this approach
using the specific case of the gravitational wave signal generated during the
inspiral phase of binary systems, modelled at the leading quadrupole Newtonian
order, in synthetic noise. We show that the method is effective in detecting
signals at the detection threshold and it is robust against (some types of)
instrumental artefacts. The computational efficiency of this method makes it
scalable to the analysis of all the triggers generated by the analysis
pipelines to search for coalescing binaries in surveys with ground-based
interferometers, and to a whole variety of signal waveforms, characterised by a
larger number of parameters.Comment: 9 page
Studying stellar binary systems with the Laser Interferometer Space Antenna using Delayed Rejection Markov chain Monte Carlo methods
Bayesian analysis of LISA data sets based on Markov chain Monte Carlo methods
has been shown to be a challenging problem, in part due to the complicated
structure of the likelihood function consisting of several isolated local
maxima that dramatically reduces the efficiency of the sampling techniques.
Here we introduce a new fully Markovian algorithm, a Delayed Rejection
Metropolis-Hastings Markov chain Monte Carlo method, to efficiently explore
these kind of structures and we demonstrate its performance on selected LISA
data sets containing a known number of stellar-mass binary signals embedded in
Gaussian stationary noise.Comment: 12 pages, 4 figures, accepted in CQG (GWDAW-13 proceedings
The effects of LIGO detector noise on a 15-dimensional Markov-chain Monte-Carlo analysis of gravitational-wave signals
Gravitational-wave signals from inspirals of binary compact objects (black
holes and neutron stars) are primary targets of the ongoing searches by
ground-based gravitational-wave (GW) interferometers (LIGO, Virgo, and
GEO-600). We present parameter-estimation results from our Markov-chain
Monte-Carlo code SPINspiral on signals from binaries with precessing spins. Two
data sets are created by injecting simulated GW signals into either synthetic
Gaussian noise or into LIGO detector data. We compute the 15-dimensional
probability-density functions (PDFs) for both data sets, as well as for a data
set containing LIGO data with a known, loud artefact ("glitch"). We show that
the analysis of the signal in detector noise yields accuracies similar to those
obtained using simulated Gaussian noise. We also find that while the Markov
chains from the glitch do not converge, the PDFs would look consistent with a
GW signal present in the data. While our parameter-estimation results are
encouraging, further investigations into how to differentiate an actual GW
signal from noise are necessary.Comment: 11 pages, 2 figures, NRDA09 proceeding
Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network
The present operation of the ground-based network of gravitational-wave laser
interferometers in "enhanced" configuration brings the search for gravitational
waves into a regime where detection is highly plausible. The development of
techniques that allow us to discriminate a signal of astrophysical origin from
instrumental artefacts in the interferometer data and to extract the full range
of information are some of the primary goals of the current work. Here we
report the details of a Bayesian approach to the problem of inference for
gravitational wave observations using a network of instruments, for the
computation of the Bayes factor between two hypotheses and the evaluation of
the marginalised posterior density functions of the unknown model parameters.
The numerical algorithm to tackle the notoriously difficult problem of the
evaluation of large multi-dimensional integrals is based on a technique known
as Nested Sampling, which provides an attractive alternative to more
traditional Markov-chain Monte Carlo (MCMC) methods. We discuss the details of
the implementation of this algorithm and its performance against a Gaussian
model of the background noise, considering the specific case of the signal
produced by the in-spiral of binary systems of black holes and/or neutron
stars, although the method is completely general and can be applied to other
classes of sources. We also demonstrate the utility of this approach by
introducing a new coherence test to distinguish between the presence of a
coherent signal of astrophysical origin in the data of multiple instruments and
the presence of incoherent accidental artefacts, and the effects on the
estimation of the source parameters as a function of the number of instruments
in the network.Comment: 22 page
Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics
We investigate the Bayesian framework for detection of continuous
gravitational waves (GWs) in the context of targeted searches, where the phase
evolution of the GW signal is assumed to be known, while the four amplitude
parameters are unknown. We show that the orthodox maximum-likelihood statistic
(known as F-statistic) can be rediscovered as a Bayes factor with an unphysical
prior in amplitude parameter space. We introduce an alternative detection
statistic ("B-statistic") using the Bayes factor with a more natural amplitude
prior, namely an isotropic probability distribution for the orientation of GW
sources. Monte-Carlo simulations of targeted searches show that the resulting
Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e. has a
higher expected detection probability at equal false-alarm probability) than
the frequentist F-statistic.Comment: 12 pages, presented at GWDAW13, to appear in CQ
Early Advanced LIGO binary neutron-star sky localization and parameter estimation
2015 will see the first observations of Advanced LIGO and the start of the
gravitational-wave (GW) advanced-detector era. One of the most promising
sources for ground-based GW detectors are binary neutron-star (BNS)
coalescences. In order to use any detections for astrophysics, we must
understand the capabilities of our parameter-estimation analysis. By simulating
the GWs from an astrophysically motivated population of BNSs, we examine the
accuracy of parameter inferences in the early advanced-detector era. We find
that sky location, which is important for electromagnetic follow-up, can be
determined rapidly (~5 s), but that sky areas may be hundreds of square
degrees. The degeneracy between component mass and spin means there is
significant uncertainty for measurements of the individual masses and spins;
however, the chirp mass is well measured (typically better than 0.1%).Comment: 4 pages, 2 figures. Published in the proceedings of Amaldi 1
Cosmic Swarms: A search for Supermassive Black Holes in the LISA data stream with a Hybrid Evolutionary Algorithm
We describe a hybrid evolutionary algorithm that can simultaneously search
for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The
algorithm mixes evolutionary computation, Metropolis-Hastings methods and
Nested Sampling. The inspiral of SMBHBs presents an interesting problem for
gravitational wave data analysis since, due to the LISA response function, the
sources have a bi-modal sky solution. We show here that it is possible not only
to detect multiple SMBHBs in the data stream, but also to investigate
simultaneously all the various modes of the global solution. In all cases, the
algorithm returns parameter determinations within (as estimated from
the Fisher Matrix) of the true answer, for both the actual and antipodal sky
solutions.Comment: submitted to Classical & Quantum Gravity. 19 pages, 4 figure
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