An Aboriginal nurse-led working model for success in graduating Indigenous Australian nurses

Australia needs more Indigenous nurses. This is widely recognised in both academic literature and government policy. In 2012, only 0.8 percent of the Australian nursing workforce was Indigenous (AIHW, 2012). In spite of the clear need, there is little discussion about how to successfully recruit, retain and graduate Indigenous nursing students. This paper describes a successful program being implemented at the University of Southern Queensland (USQ). Between 2000 and 2012, USQ graduated 80 Indigenous nurses and midwives, at both undergraduate and postgraduate levels. In this paper, the authors outline the journey they undertook to develop the successful program at USQ: the Indigenous Nursing Support (INS) Model: Helping Hands. They argue that four elements underpin success for Indigenous nursing students: the availability of Indigenous academics, Indigenous health content in the nursing curriculum, Indigenous-specific recruitment materials, and individual mentoring and nurturing of Indigenous students.

Secondary structure element alignment kernel method for prediction of protein structural classes

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.

Perinatal nutrition and gastrointestinal disorders : Working Group Report of the Second World Congress of Pediatric Gastroenterology, Hepatology, and Nutrition

Metabolic programming during the perinatal period as a consequence of early nutrition is an emerging area of great interest. This concept is known as the "fetal origins of adult disease" theory (1). Numerous epidemiological studies published over the past 20 years or so have suggested that small body size at birth and during infancy and, more specifically, intrauterine growth retardation are associated later in life with lowered cognitive performance and increased rates of coronary heart disease and its major biological risk factors, ie, raised blood pressure, insulin resistance, coronary artery disease, and abnormalities in lipid metabolism. The molecular mechanisms that govern this phenomenon in humans, however, are unknown and need to be elucidated.

Quantile regression for longitudinal data with a working correlation model

This paper proposes a linear quantile regression analysis method for longitudinal data that combines the between- and within-subject estimating functions, which incorporates the correlations between repeated measurements. Therefore, the proposed method results in more efficient parameter estimation relative to the estimating functions based on an independence working model. To reduce computational burdens, the induced smoothing method is introduced to obtain parameter estimates and their variances. Under some regularity conditions, the estimators derived by the induced smoothing method are consistent and have asymptotically normal distributions. A number of simulation studies are carried out to evaluate the performance of the proposed method. The results indicate that the efficiency gain for the proposed method is substantial especially when strong within correlations exist. Finally, a dataset from the audiology growth research is used to illustrate the proposed methodology.

Working covariance model selection for generalized estimating equations

We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice.

Discussion of “Generalized estimating equations: Notes on the choice of the working correlation matrix”

Objective To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens "Generalized Estimating Equations. Notes on the Choice of the Working Correlation Matrix". Methods Inviting an international group of experts to comment on this paper. Results Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data Applied statisticians; commented on practical aspects in data analysis. Conclusions In general, careful modeling correlation is encouraged when considering estimation efficiency and other implications, and a comparison of choosing instruments in GMM and generalized estimating equations, (GEE) would be worthwhile. Some theoretical drawbacks of GEE need to be further addressed and require careful analysis of data This particularly applies to the situation when data are missing at random.

Working-correlation-structure identification in generalized estimating equations

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well-known criterion of QIC for selecting a working correlation Structure. and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads LIS to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study.

Criteria for working-correlation-structure selection in GEE: Assessment via simulation

Efficiency of analysis using generalized estimation equations is enhanced when intracluster correlation structure is accurately modeled. We compare two existing criteria (a quasi-likelihood information criterion, and the Rotnitzky-Jewell criterion) to identify the true correlation structure via simulations with Gaussian or binomial response, covariates varying at cluster or observation level, and exchangeable or AR(l) intracluster correlation structure. Rotnitzky and Jewell's approach performs better when the true intracluster correlation structure is exchangeable, while the quasi-likelihood criteria performs better for an AR(l) structure.

Unbiased estimating equations from working correlation models for irregularly timed repeated measures

The method of generalized estimating equation-, (GEEs) has been criticized recently for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. However, the feasibility and efficiency of GEE methods can be enhanced considerably by using flexible families of working correlation models. We propose two ways of constructing unbiased estimating equations from general correlation models for irregularly timed repeated measures to supplement and enhance GEE. The supplementary estimating equations are obtained by differentiation of the Cholesky decomposition of the working correlation, or as score equations for decoupled Gaussian pseudolikelihood. The estimating equations are solved with computational effort equivalent to that required for a first-order GEE. Full details and analytic expressions are developed for a generalized Markovian model that was evaluated through simulation. Large-sample ".sandwich" standard errors for working correlation parameter estimates are derived and shown to have good performance. The proposed estimating functions are further illustrated in an analysis of repeated measures of pulmonary function in children.

Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance

The method of generalised estimating equations for regression modelling of clustered outcomes allows for specification of a working matrix that is intended to approximate the true correlation matrix of the observations. We investigate the asymptotic relative efficiency of the generalised estimating equation for the mean parameters when the correlation parameters are estimated by various methods. The asymptotic relative efficiency depends on three-features of the analysis, namely (i) the discrepancy between the working correlation structure and the unobservable true correlation structure, (ii) the method by which the correlation parameters are estimated and (iii) the 'design', by which we refer to both the structures of the predictor matrices within clusters and distribution of cluster sizes. Analytical and numerical studies of realistic data-analysis scenarios show that choice of working covariance model has a substantial impact on regression estimator efficiency. Protection against avoidable loss of efficiency associated with covariance misspecification is obtained when a 'Gaussian estimation' pseudolikelihood procedure is used with an AR(1) structure.

Impact of working conditions on the quality of working life: Case manufacturing sector colombian Caribbean Region [Impacto de las Condiciones de Trabajo en Calidad de Vida Laboral: Caso del Sector Manufacturero de la Región Caribe Colombiana]

La presenta investigación centra su atención en evaluar el impacto de las Condiciones de Trabajo en la Calidad de Vida Laboral del talento humano de sector manufacturero de la región Caribe colombiana. Para analizar este proceso se entrevistaron a 518 empleados del sector. El diseño utilizado fue no experimental de tipo transversal descriptivo, puesto que a cada participante se le aplicó una entrevista con el instrumento de Condiciones de Trabajo y la Herramienta de Calidad de Vida Laboral (Condiciones Salariales y Subjetivas). Los datos fueron analizados mediante análisis de correlación y modelos de regresión logística. Los resultados mostraron que el ambiente térmico y las normas de seguridad en el trabajo afectan de forma positiva la Calidad de Vida Laboral de los empleados del sector. Estos resultados ponen de manifiesto que la relación entre las condiciones de trabajo y la CVL se basa en la competencia y distan de ser una relación lineal y simple relacionada con la consideración de la presencia o la ausencia de las condiciones de trabajo. Ello tiene implicaciones a la hora de formular políticas, programas e intervenciones para prevenir, erradicar y amortiguar los efectos negativos de las condiciones de trabajo y mejorar la seguridad industrial dentro de las empresas.

Different countries, same science classes: Students’ experiences of school science in their own words

This paper reviews the remarkably similar experiences of school science reported by high school students in Sweden, England, and Australia. It compares student narratives from interpretive studies by Lindahl, by Osborne and Collins, and by Lyons, identifying core themes relating to critical contemporary issues in science education. These themes revolve around the transmissive pedagogy, decontextualized content, and unnecessary difficulty of school science commonly reported by students in the studies. Their collective experiences are used as a framework for examining student conceptions of, and attitudes to, school science more generally, drawing on an extensive range of international literature. The paper argues that the experiences of students in the three studies provide important insights into the widespread declines in interest and enrolments in high school and university science courses.

Patient Safety - A Psychological Perspective

This comprehensive book takes a psychological perspective on patient safety. It is based on the most recent theoretical and empirical research evidence from psychology (including clinical, work, and organizational psychology) and adjacent social and behavioral sciences such as human factors. Factors that influence safety-related experiences, behaviors, and outcomes of patients and professionals working in clinical settings such as medical practices and hospitals are reviewed, structured, and critically evaluated. Consistent with the complexity of the topic, the author takes a multi-level approach to patient safety, which includes a review of individual, team, and organizational factors and outcomes. The book describes how these factors, by themselves and in combination, can facilitate or impede patient safety. Individual factors include safety-relevant knowledge, skills, abilities, and personality traits such as conscientiousness and emotional stability. Team factors include group communication, training, and leadership. Finally, organizational factors include the safety culture and climate. Throughout the book, different evidence-based intervention programs are described that can help practitioners promote patient safety and prevent accidents. The book is a valuable resource for both researchers and practitioners interested in understanding, maintaining, and improving patient safety in a variety of applied settings. It is based on the most up-to-date research evidence from psychology and neighboring disciplines, and it is written in a clear and non-technical language understandable for a wide audience.