Migration decision making and migration behavior in rural Kenya

Micro theories of migration have generally been tested with data which do not allow one to distinguish between the decision to move and the act of moving. The latter is usually treated as a proxy for the former. Using data from a two round survey of youth in rural Kenya we classify respondents as decision processors/nondecision processors from round one data, and determine who migrates over a nine month interval from round two data. Thirty-four percent of the sample are classified as decision processors and 25% as migrants, but decision processors were only slightly more likely to migrate than nondecision processors and more migrants actually came from the latter group.A simple model is developed to determine the extent to which the same factors that influence decision-processing influence who migrates. The model is tested using regression procedures by first treating decision-processing as the dependent variable then migration. The model explains 26% of the variance in who decides, but only 10% of the variance in who actually moved. Finally, separate analyses are done of the determinants of migration for decision processors and nondecision processors.     

The component structure of elderly population growth in the Netherlands: 1950–1980

While there is no doubt about the magnitude of the growth of older age groups in industrialized nations, its structural components are less well understood. This paper examines the roles played by cohort succession and mortality in the process for the Netherlands during the period 1950 to 1980. While the size of population aged 65 and over has increased for both males and females, increases in the size of succeeding cohorts explain all of the growth in the older male population. Reductions in mortality at the older old ages play a much greater role than do reductions at younger old ages; the latter reductions occur among females only.     

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.     

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.