Estimation in capture–recapture models when covariates are subject to measurement errors and missing data

For capture–recapture models when covariates are subject to measurement errors and missing data, a set of estimating equations is constructed to estimate population size and relevant parameters. These estimating equations can be solved by an algorithm similar to the EM algorithm. The proposed method is also applicable to the situation when covariates with no measurement errors have missing data. Simulation studies are used to assess the performance of the proposed estimator. The estimator is also applied to a capture–recapture experiment on the bird species Prinia flaviventris in Hong Kong. The Canadian Journal of Statistics 37: 645–658; 2009 © 2009 Statistical Society of Canada     

A robust prediction error criterion for pareto modelling of upper tails

Estimation of the Pareto tail index from extreme order statistics is an important problem in many settings. The upper tail of the distribution, where data are sparse, is typically fitted with a model, such as the Pareto model, from which quantities such as probabilities associated with extreme events are deduced. The success of this procedure relies heavily not only on the choice of the estimator for the Pareto tail index but also on the procedure used to determine the number k of extreme order statistics that are used for the estimation. The authors develop a robust prediction error criterion for choosing k and estimating the Pareto index. A Monte Carlo study shows the good performance of the new estimator and the analysis of real data sets illustrates that a robust procedure for selection, and not just for estimation, is needed.     

A decision support system for banks

Just recently, information systems with rather unique characteristics have begun to emerge. These systems usually referred to as Decision Support Systems, feature decision models, a data base and the decision maker as subsystems and are specifically oriented to supporting decision making. This article examines Decision Support Systems and their application to banks.     

History lessons: inequality,diversity and the national curriculum

This article explores the continued importance of teaching a diverse curriculum at a time when issues of racial and ethnic equality and diversity have been increasingly sidelined in the political discussion around ‘British’ values and identities, and how these should be taught in schools. The 2014 History National curriculum, in particular, provoked widespread controversy around what British history is, who gets included in this story and how best to engage young people in increasingly diverse classrooms with the subject. The new curriculum provides both opportunities for, and constraints on, addressing issues of racial and ethnic equality and diversity, but how these are put into practice in an increasingly fragmented school system remains less clear. Drawing on the findings of two research projects in schools across England and Wales, this article examines the challenges and opportunities facing teachers and young people in the classroom in the teaching and learning of diverse British histories. We argue that it is not only the content of what children and young people are taught in schools that is at issue, but how teachers are supported to teach diverse curricula effectively and confidently.     

The Use of Common Humanity Scenarios to Promote Compassion in Healthcare Workers

ABSTRACT

The importance of compassion in healthcare is universally acknowledged. However, the factors that enhance compassion are not well understood. The perception of common humanity has been proposed as a prosocial perspective that leads to unbiased universal compassion. There has been a lack of research into the relationship between common humanity and compassion. This study examined the use of common humanity scenarios to promote compassion in healthcare workers. Seventy-five healthcare workers were randomly assigned to two groups and shown a different common humanity scenario. The healthcare workers were asked what effect viewing the videos had on their perception of common humanity and compassion. Thematic analysis was used to identify themes. Four main themes emerged: (a) common bonds; (b) people have the same needs; (c) no one wants to suffer; (d) seeing strangers helping others is motivating. Healthcare workers reported feelings of care, concern, and compassion after viewing common humanity scenarios.

IMPLICATIONS

• Viewing common humanity scenarios appears to lead to enhanced feelings of connection to others and compassion in healthcare workers.

• Using common humanity scenarios may be a useful compassion training strategy in healthcare.

• Further research is necessary to gain a better understanding of how viewing common humanity material influences compassion.