Haemodialysis work environment contributors to job satisfaction and stress: A sequential mixed methods study

Background: Haemodialysis nurses form long term relationships with patients in a technologically complex work environment. Previous studies have highlighted that haemodialysis nurses face stressors related to the nature of their work and also their work environments leading to reported high levels of burnout. Using Kanters (1997) Structural Empowerment Theory as a guiding framework, the aim of this study was to explore the factors contributing to satisfaction with the work environment, job satisfaction, job stress and burnout in haemodialysis nurses. Methods: Using a sequential mixed-methods design, the first phase involved an on-line survey comprising demographic and work characteristics, Brisbane Practice Environment Measure (B-PEM), Index of Work Satisfaction(IWS), Nursing Stress Scale (NSS) and the Maslach Burnout Inventory (MBI). The second phase involved conducting eight semi-structured interviews with data thematically analyzed. Results: From the 417 nurses surveyed the majority were female (90.9 %), aged over 41 years of age (74.3 %), and 47.4 % had worked in haemodialysis for more than 10 years. Overall the work environment was perceived positively and there was a moderate level of job satisfaction. However levels of stress and emotional exhaustion (burnout) were high. Two themes, ability to care and feeling successful as a nurse, provided clarity to the level of job satisfaction found in phase 1. While two further themes, patients as quasi-family and intense working teams, explained why working as a haemodialysis nurse was both satisfying and stressful. Conclusions: Nurse managers can use these results to identify issues being experienced by haemodialysis nurses working in the unit they are supervising.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.