Confidence interval estimation of the difference between paired AUCs based on combined biomarkers

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.     

Using relative utility curves to evaluate risk prediction

Summary.  Because many medical decisions are based on risk prediction models that are constructed from medical history and results of tests, the evaluation of these prediction models is important. This paper makes five contributions to this evaluation: the relative utility curve which gauges the potential for better prediction in terms of utilities, without the need for a reference level for one utility, while providing a sensitivity analysis for misspecification of utilities, the relevant region, which is the set of values of prediction performance that are consistent with the recommended treatment status in the absence of prediction, the test threshold, which is the minimum number of tests that would be traded for a true positive prediction in order for the expected utility to be non-negative, the evaluation of two-stage predictions that reduce test costs and connections between various measures of performance of prediction. An application involving the risk of cardiovascular disease is discussed.     

浅析民办高校学生人格教育意义及途径

当前,我国民办高校快速发展,学生人数大量增加,民办高校大学生这一特殊群体的素质教育问题逐渐引起人们的重视。通过分析民办高校专业及课程设置的特点及学生的思想现状．指出人格教育对于民办高校大学生素质提高和全面和谐发展具有特殊重要的意义,从重视传统文化教育等角度提出民办高校大学生人格教育的几种途径。     

人口计生系统学习型组织推广的思考

人口与计划生育系统学习型组织由“点”到“面”的推广是完全有必要的。在推广的早期、中期和后期各有几项重点工作,包括完善人口与计生目标管理责任制的指标体系,普及学习方法和学习工具的应用,强化学习型组织推广的评估。这些推广工作将有助于人口计生综合改革的深入发展。     

Report on the implementation and evaluation of an interprofessional learning programme for inter‐agency child protection teams

Effective collaborative working between the different professions and agencies working to safeguard children is essential. Lack of knowledge of others' roles, perceived differences in status and expertise, and negative stereotypes are examples of why this can be difficult to achieve. A facilitated interprofessional learning (IPL) programme was implemented to assess if such an intervention could overcome some of the barriers to effective interprofessional and inter‐agency team working. Six teams based in primary and secondary care trusts took part with participants from health, education, police and social services. Quantitative and qualitative data were collected from individual participants using a pre‐validated team climate inventory and reflective statements. Findings from this study demonstrate statistically significant changes in five out of six categories in the inventory. Reflective statements suggested that overall the programme offered a positive learning experience at both an individual and team level. The importance of outside facilitation to maintain the team's momentum was acknowledged, as was the difficulty of maintaining a good level of activity once the programme had ended. Nevertheless, the IPL programme provided a timely opportunity to reflect upon the interprofessional and inter‐agency team working needed within Children's Trusts and the implications for staff involved with regard to skills development. Copyright © 2009 John Wiley & Sons, Ltd.     

Machine Learning for Drug Overdose Surveillance

ABSTRACT

We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan (Herlands, McFowland, Wilson, & Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) enables early detection of emerging patterns in spatio-temporal data, accounting for both the complex, correlated nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan (Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) to 8 years of case-level overdose data from Allegheny County, Pennsylvania. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes.     

When difference creates dissonance: understanding the ‘engine’ of intercultural learning in study abroad

Why do some study abroad students improve their intercultural skills, while others revert to less sophisticated ways of making sense of cultural difference? Both intercultural competence theory and transformative learning theory attempt to explain why student intercultural learning occurs, but they only provide partial answers. Building on our previous study assessing intercultural competence in a 2015 field school in India, this article applies the concept of cognitive dissonance to explain the process behind intercultural learning. In the context of study abroad, students experience cognitive dissonance when they encounter cultural differences or similarities that confound previously held expectations about culture. Adapting Maertz, Hassan, and Magnusson’s cognitive dissonance resolution framework, we employ qualitative analysis of students’ written reflections to show how the resolution of cognitive dissonance could act as the ‘engine’ of intercultural learning.     

Possibilities towards a socially just pedagogy: New tasks and challenges

This paper contributes to an emerging debate on what it means to transform teaching and learning in higher education and to renew the social justice mission of universities. By focusing on the teaching and learning environment, we set the stage for the special issue, exploring the idea of socially just pedagogy, where it comes from and what it means in the present day. The social justice-pedagogy nexus as taken up in this paper aims at using pedagogy as a change process to transform higher education. We offer new considerations arguing that to adopt socially just pedagogies it is important that teachers consider the personal dimensions of pedagogy, the politics of difference, and the relationship between pedagogy and agency. In doing this, the chance that universities remain places of possibility, rather than turning into divisive spaces, becomes more plausible.     

Disequilibrium multi-dividing ontology learning algorithm

As an effective tool for data storage, processing, and computing, ontology has been used in many fields of computer science and information technology. By means of its powerful performance on semantic query and knowledge extraction, domain ontology has been built on various disciplines such as biology, pharmaceutics, geography, chemistry, etc. and been smoothly employed for their engineering applications. In these ontology applications, we aim to get an optimal ontology function which maps each ontology to a real number and then determine the similarity between concepts by the distance of their corresponding real numbers. In former ontology learning approaches, all the instances in the training sample have equal status in the learning process. In this article, we present the disequilibrium multi-dividing ontology algorithm in which the important ontology data will be highlighted during the learning, and the relevant ontology data tend to be eliminated. Four experiments are designed to test the serviceability of our disequilibrium multi-dividing algorithm from angles of ontology similarity measuring and ontology mapping construction.     

Child learning and family mealtime environment,parenting style and family functioning among preschoolers in Hong Kong

A two-wave longitudinal study on 470 parent–child dyads examined the impact of family mealtime environment, parenting style and family functioning on preschoolers’ (three to six years old) learning. Measures included parent report on parenting style, family functioning, family meal frequency, mealtime television, feeding practice, teacher/parent report of academic competence and individual assessment of preschoolers’ preschool concepts. Family mealtime environment variables were associated with pre-schoolers’ learning, in addition to parenting style and family functioning, after controlling for family income and wave 1 preschoolers’ learning. The results highlighted the importance of family mealtime environment on preschoolers’ learning.