The role played by a range of psychological variables in nurses’ handwashing behaviour

Background: Psychological models of behaviour change are used to predict patients’ health behaviours but have rarely been used to explore healthcare professionals’ health-related behaviour. Aim: To explore the association between self-reported handwashing and a range of psychological variables in a sample of nurses in a large acute hospital. Results and discussion: Nurses in this study were more likely to wash their hands if they perceived it to be important and if they thought their workplace helped them in doing so. The best predictor of perceived importance was how strongly a nurse believed that poor handwashing practice contributes to spreading infection. Conclusion: In this study, psychological variables such as perception of importance, perception of workplace support, occupational stress and perception of risk were important predictors of handwashing behaviour.

Optic variables used to judge future ball arrival position in expert and novice soccer players

Although many studies have looked at the perceptual-cognitive strategies used to make anticipatory judgments in sport, few have examined the informational invariants that our visual system may be attuned to. Using immersive interactive virtual reality to simulate the aerodynamics of the trajectory of a ball with and without sidespin, the present study examined the ability of expert and novice soccer players to make judgments about the ball's future arrival position. An analysis of their judgment responses showed how participants were strongly influenced by the ball's trajectory. The changes in trajectory caused by sidespin led to erroneous predictions about the ball's future arrival position. An analysis of potential informational variables that could explain these results points to the use of a first-order compound variable combining optical expansion and optical displacement.

Homogeneous habitat can meet the discrete and varied resource requirements of hares but may set an ecological trap

In common with other farmland species, hares (Lepus spp.) are in widespread decline in agricultural landscapes due to agricultural intensification and habitat loss. We examined the importance of habitat heterogeneity to the Irish hare (Lepus timidus hibernicus) in a pastoral landscape. We used radio-tracking during nocturnal active and diurnal inactive periods throughout one year. In autumn, winter and spring, hares occupied a heterogeneous combination of improved grassland, providing food, and Juncus-dominated rough pasture, providing refuge. In summer, hares significantly increased their use of improved grassland. This homogeneous habitat can fulfil the discrete and varied resource requirements of hares for feeding and shelter at certain times of year. However, improved grassland may be a risky habitat for hares as silage harvesting occurs during their peak birthing period of late spring and early summer. We therefore posit the existence of a putative ecological trap inherent to a homogeneous habitat of perceived high value that satisfies the hares' habitat requirements but which presents risks at a critical time of year. To test this hypothesis in relation to hare populations, work is required to provide data on differential leveret mortality between habitat types.

SuperWASP observations of long timescale photometric variations in cataclysmic variables

Aims: We investigated whether the predictions and results of Stanishev et al. (2002, A&A, 394, 625) concerning a possible relationship between eclipse depths in PX And and its retrograde disc precession phase, could be confirmed in long term observations made by SuperWASP. In addition, two further CVs (DQ Her and V795 Her) in the same SuperWASP data set were investigated to see whether evidence of superhump periods and disc precession periods were present and what other, if any, long term periods could be detected. Methods: Long term photometry of PX And, V795 Her and DQ Her was carried out and Lomb-Scargle periodogram analysis undertaken on the resulting light curves. For the two eclipsing CVs, PX And and DQ Her, we analysed the potential variations in the depth of the eclipse with cycle number. Results: The results of our period and eclipse analysis on PX And confirm that the negative superhump period is 0.1417 ± 0.0001d. We find no evidence of positive superhumps in our data suggesting that PX And may have been in a low state during our observations. We improve on existing estimates of the disc precession period and find it to be 4.43 ± 0.05d. Our results confirm the predictions of Stanishev et al. (2002). We find that DQ Her does not appear to show a similar variation for we find no evidence of negative superhumps or of a retrograde disc precession. We also find no evidence of positive superhumps or of a prograde disc precession and we attribute the lack of positive superhumps in DQ Her to be due to the high mass ratio of this CV. We do however find evidence for a modulation of the eclipse depth over a period of 100 days which may be linked with solar-type magnetic cycles which give rise to long term photometric variations. The periodogram analysis for V795 Her detected the likely positive superhump period 0.1165d, however, neither the 0.10826d orbital period nor the prograde 1.53d disc precession period were seen. Here though we have found a variation in the periodogram power function at the positive superhump period, over a period of at least 120 days.

Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.