Exploring the shift to an improvement-oriented approach to external evaluation in Aotearoa New Zealand: The case of the Education Review Office

External evaluation in Aotearoa New Zealand is an important accountability mechanism in education. In 2019 the Tomorrow’s Schools Independent Taskforce recommended that the Education Review Office (ERO) develop and implement an improvement-oriented approach to external evaluation in schools. This approach requires fundamental shifts in evaluation practice. In implementing an improvement-oriented approach, while maintaining accountability functions in a public sector context, evaluators need to balance key tensions: relational, epistemological, pedagogical, contextual, political, methodological, and organisational. The role of the evaluator in implementing ERO’s new approach, and managing the shifts required, is key to the approach’s success. Building evaluation capability and capacity and strengthening the evaluation evidence base are critical areas for further development

Bundap Marram Durn Durn: Engagement with Aboriginal women experiencing comorbid chronic physical and mental health conditions

OBJECTIVE: To explore antecedents of health service engagement and experience among urban Aboriginal people with comorbid physical and mental health conditions. METHODS: Focus groups and interviews were conducted with Aboriginal people who had comorbid health conditions and were accessing Aboriginal and/or mainstream services. RESULTS: Nineteen participants, all women, were recruited. Participants' personal histories and prior experience of health services affected effective service utilisation. Participants' service experiences were characterised by long waiting times in the public health system and high healthcare staff turnover. Trusted professionals were able to act as brokers to other clinically and culturally competent practitioners. CONCLUSIONS: Many urban Aboriginal women attended health services with multiple comorbid conditions including chronic disease and mental health issues. Several barriers and enablers were identified concerning the capacity of services to engage and effectively manage Aboriginal patients' condition

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

Validity of new child-specific thoracic gas volume prediction equations for air-displacement plethysmography

BACKGROUND: To determine the validity of the recently developed child-specific thoracic gas volume (TGV) prediction equations for use in air-displacement plethysmography (ADP) in diverse pediatric populations. METHODS: Three distinct populations were studied: European American and African American children living in Birmingham, Alabama and European children living in Lisbon, Portugal. Each child completed a standard ADP testing protocol, including a measured TGV according to the manufactures software criteria. Measured TGV was compared to the predicted TGV from current adult-based ADP proprietary equations and to the recently developed child-specific TGV equations of Fields et al. Similarly, percent body fat, derived using the TGV prediction equations, was compared to percent body fat derived using measured TGV. RESULTS: Predicted TGV from adult-based equations was significantly different from measured TGV in girls from each of the three ethnic groups (P < 0.05), however child-specific TGV estimates did not significantly differ from measured TGV in any of the ethnic or gender groups. Percent body fat estimates using adult-derived and child-specific TGV estimates did not differ significantly from percent body fat measures using measured TGV in any of the groups. CONCLUSION: The child-specific TGV equations developed by Fields et al. provided a modest improvement over the adult-based TGV equations in an ethnically diverse group of children

Towards comprehensive assessment of mitral regurgitation using cardiovascular magnetic resonance

Cardiovascular magnetic resonance (CMR) is increasingly used to assess patients with mitral regurgitation. Its advantages include quantitative determination of ventricular volumes and function and the mitral regurgitant fraction, and in ischemic mitral regurgitation, regional myocardial function and viability. In addition to these, identification of leaflet prolapse or restriction is necessary when valve repair is contemplated. We describe a systematic approach to the evaluation of mitral regurgitation using CMR which we have used in 149 patients with varying etiologies and severity of regurgitation over a 15 month period

Bimodal crystallization at polymer-fullerene interfaces

The growth-kinetics of [6,6]-phenyl C61-butyric acid methyl ester (PCBM) crystals, on two different length-scales, is shown to be controlled by the thickness of the polymer layer within a PCBM-polymer bilayer. Using a model amorphous polymer we present evidence, from in situ optical microscopy and grazing-incidence X-ray diffraction (GIXD), that an increased growth-rate of nanoscale crystals impedes the growth of micron-sized, needle-like PCBM crystals. A combination of neutron reflectivity and GIXD measurements, also allows us to observe the establishment of a liquid-liquid equilibrium composition-profile between the PCBM layer and a polymer-rich layer, before crystallization occurs. While the interfacial composition-profile is independent of polymer-film-thickness, the growth-rate of nanoscale PCBM crystals is significantly larger for thinner polymer films. A similar thickness-dependent behavior is observed for different molecular weights of entangled polymer. We suggest that the behavior may be related to enhanced local-polymer-chain-mobility in nanocomposite thin-films

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

CLUSS: Clustering of protein sequences based on a new similarity measure

<p>Abstract</p> <p>Background</p> <p>The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-free algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families. In the rest of the paper, we use the term "<it>phylogenetic</it>" in the sense of "<it>relatedness of biological functions</it>".</p> <p>Results</p> <p>To show the effectiveness of CLUSS, we performed an extensive clustering on COG database. To demonstrate its ability to deal with hard-to-align sequences, we tested it on the GH2 family. In addition, we carried out experimental comparisons of CLUSS with a variety of mainstream algorithms. These comparisons were made on hard-to-align and easy-to-align protein sequences. The results of these experiments show the superiority of CLUSS in yielding clusters of proteins with similar functional activity.</p> <p>Conclusion</p> <p>We have developed an effective method and tool for clustering protein sequences to meet the needs of biologists in terms of phylogenetic analysis and prediction of biological functions. Compared to existing clustering methods, CLUSS more accurately highlights the functional characteristics of the clustered families. It provides biologists with a new and plausible instrument for the analysis of protein sequences, especially those that cause problems for the alignment-dependent algorithms.</p

Prediction of binding hot spot residues by using structural and evolutionary parameters

In this work, we present a method for predicting hot spot residues by using a set of structural and evolutionary parameters. Unlike previous studies, we use a set of parameters which do not depend on the structure of the protein in complex, so that the predictor can also be used when the interface region is unknown. Despite the fact that no information concerning proteins in complex is used for prediction, the application of the method to a compiled dataset described in the literature achieved a performance of 60.4%, as measured by F-Measure, corresponding to a recall of 78.1% and a precision of 49.5%. This result is higher than those reported by previous studies using the same data set