The social negotiation of fitness for work: tensions in doctor-patient relationships over medical certification of chronic pain

The UK government is promoting the health benefits of work, in order to change doctors' and patients' behaviour and reduce sickness absence. The rationale is that many people 'off sick' would have better outcomes by staying at work; but reducing the costs of health care and benefits is also an imperative. Replacement of the 'sick note' with the 'fit note' and a national educational programme are intended to reduce sickness-certification rates, but how will these initiatives impact on doctor-patient relationships and the existing tension between the doctor as patient advocate and gate-keeper to services and benefits? This tension is particularly acute for problems like chronic pain where diagnosis, prognosis and work capacity can be unclear. We interviewed 13 doctors and 30 chronic pain patients about their experiences of negotiating medical certification for work absence and their views of the new policies. Our findings highlight the limitations of naïve rationalist approaches to judgements of work absence and fitness for work for people with chronic pain. Moral, socio-cultural and practical factors are invoked by doctors and patients to contest decisions, and although both groups support the fit note's focus on capacity, they doubt it will overcome tensions in the consultation. Doctors value tacit skills of persuasion and negotiation that can change how patients conceptualise their illness and respond to it. Policy-makers increasingly recognise the role of this tacit knowledge and we conclude that sick-listing can be improved by further developing these skills and acknowledging the structural context within which protagonists negotiate sick-listin

High-dimensional Ising model selection using ℓ1{\ell_1}-regularized logistic regression

We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an $\ell_1$-constraint. The method is analyzed under high-dimensional scaling in which both the number of nodes $p$ and maximum neighborhood size $d$ are allowed to grow as a function of the number of observations $n$. Our main results provide sufficient conditions on the triple $(n,p,d)$ and the model parameters for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. With coherence conditions imposed on the population Fisher information matrix, we prove that consistent neighborhood selection can be obtained for sample sizes $n=\Omega(d^3\log p)$ with exponentially decaying error. When these same conditions are imposed directly on the sample matrices, we show that a reduced sample size of $n=\Omega(d^2\log p)$ suffices for the method to estimate neighborhoods consistently. Although this paper focuses on the binary graphical models, we indicate how a generalization of the method of the paper would apply to general discrete Markov random fields.Comment: Published in at http://dx.doi.org/10.1214/09-AOS691 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Asymptotic silence-breaking singularities

We discuss three complementary aspects of scalar curvature singularities: asymptotic causal properties, asymptotic Ricci and Weyl curvature, and asymptotic spatial properties. We divide scalar curvature singularities into two classes: so-called asymptotically silent singularities and non-generic singularities that break asymptotic silence. The emphasis in this paper is on the latter class which have not been previously discussed. We illustrate the above aspects and concepts by describing the singularities of a number of representative explicit perfect fluid solutions.Comment: 25 pages, 6 figure

Adapting structuration theory to understand the role of reflexivity: Problematization, clinical audit and information systems

This paper is an exploratory account of the further development and application of a hybrid framework, StructurANTion, that is based on Structuration Theory and Actor Network Theory (ANT). The use of social theories in general and their use in information systems (IS) research in particular is explored leading to the use of the framework to examine the concept of what are termed humanchine networks in the context of clinical audit, within a healthcare Primary Care Trust (PCT). A particular focus is on the manner in which information systems-based reflexivity contributes to both entrenching a networks’ structurated order as well as contributing to its emancipatory change. The case study compares clinic-centric and patientcentric audit and seeks to further extend the understanding of the role of information and information systems within structurated humanchine activity systems. Conclusions indicate that the use of more socially informed IS methods and approaches can incorporate more emancipatory ideals and lead to greater adoption and usage of more relevant and useful clinical information systems and practices

Curvature blow up in Bianchi VIII and IX vacuum spacetimes

The maximal globally hyperbolic development of non-Taub-NUT Bianchi IX vacuum initial data and of non-NUT Bianchi VIII vacuum initial data is C2 inextendible. Furthermore, a curvature invariant is unbounded in the incomplete directions of inextendible causal geodesics.Comment: 20 pages, no figures. Submitted to Classical and Quantum Gravit

Geographic Gossip: Efficient Averaging for Sensor Networks

Gossip algorithms for distributed computation are attractive due to their simplicity, distributed nature, and robustness in noisy and uncertain environments. However, using standard gossip algorithms can lead to a significant waste in energy by repeatedly recirculating redundant information. For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is related to the slow mixing times of random walks on the communication graph. We propose and analyze an alternative gossiping scheme that exploits geographic information. By utilizing geographic routing combined with a simple resampling method, we demonstrate substantial gains over previously proposed gossip protocols. For regular graphs such as the ring or grid, our algorithm improves standard gossip by factors of $n$ and $\sqrt{n}$ respectively. For the more challenging case of random geometric graphs, our algorithm computes the true average to accuracy $\epsilon$ using $O(\frac{n^{1.5}}{\sqrt{\log n}} \log \epsilon^{-1})$ radio transmissions, which yields a $\sqrt{\frac{n}{\log n}}$ factor improvement over standard gossip algorithms. We illustrate these theoretical results with experimental comparisons between our algorithm and standard methods as applied to various classes of random fields.Comment: To appear, IEEE Transactions on Signal Processin

Exhaust jet wake and thrust characteristics of several nozzles designed for VTOL DOWNWASH suppression. Tests in and out of ground effect with 70 deg F and 1200 deg F nozzle discharge temperatures

Jet wake degradation and thrust characteristics of exhaust nozzles designed for VTOL downwash suppression and fuselage and ground effect

An Ecological Risk Model for Early Childhood Anxiety: The Importance of Early Child Symptoms and Temperament

Childhood anxiety is impairing and associated with later emotional disorders. Studying risk factors for child anxiety may allow earlier identification of at-risk children for prevention efforts. This study applied an ecological risk model to address how early childhood anxiety symptoms, child temperament, maternal anxiety and depression symptoms, violence exposure, and sociodemographic risk factors predict school-aged anxiety symptoms. This longitudinal, prospective study was conducted in a representative birth cohort (n=1109). Structural equation modeling was used to examine hypothesized associations between risk factors measured in toddlerhood/preschool (age=3.0 years) and anxiety symptoms measured in kindergarten (age=6.0 years) and second grade (age= 8.0 years). Early child risk factors (anxiety symptoms and temperament) emerged as the most robust predictor for both parent-and child-reported anxiety outcomes and mediated the effects of maternal and family risk factors. Implications for early intervention and prevention studies are discussed