Rolling with the shakes: an insight into teenagers’ perceptions of recovery after the Canterbury earthquakes

Natural disasters are harmful worldwide events that inflict multiple psychosocial impacts on disaster-exposed individuals. A significant proportion of affected individuals are teenagers (13–18 years old) who, compared with adults, have been historically overlooked in disaster research. The literature is particularly sparse concerning teenagers’ recovery from natural disasters, specifically what recovery means to them and the contributing factors towards their positive recovery. Therefore, the aim of the current study was to gain deeper insight into this largely unexplored area by conducting five focus groups with teenagers (16–18 years) who experienced at least one of the Canterbury, New Zealand, earthquakes since and including the initial September 2010 earthquake. This study directly asked teenagers about their recovery over the three years after the initial earthquakes, with data being analysed using thematic analysis. Two main themes were identified: (1) perceptions of recovery, with three sub-themes (i) knowledge and being less frightened, (ii) talking about the earthquakes and (iii) shift in perspectives; and (2) contributing factors to recovery, with three sub-themes (i) participation in the community response, (ii) returning to school and (iii) the rebuild of Christchurch. These factors provide insight into how we can better support the recovery process for disaster-exposed teenagers to reduce long-term distress.     

Practical usage of O'Brien's OLS and GLS statistics in clinical trials

Multivariate techniques of O'Brien's OLS and GLS statistics are discussed in the context of their application in clinical trials. We introduce the concept of an operational effect size and illustrate its use to evaluate power. An extension describing how to handle covariates and missing data is developed in the context of Mixed models. This extension allowing adjustment for covariates is easily programmed in any statistical package including SAS. Monte Carlo simulation is used for a number of different sample sizes to compare the actual size and power of the tests based on O'Brien's OLS and GLS statistics.     

‘Brain circulation’ and transnational knowledge networks: studying long‐term effects of academic mobility to Germany, 1954–2000

‘Brain circulation’ has become a buzzword for describing the increasingly networked character of highly skilled migration. In this article, the concept is linked to academics' work on circular mobility to explore the long‐term effects of their research stays in Germany during the second half of the twentieth century. Based on original survey data on more than 1800 former visiting academics from 93 countries, it is argued that this type of brain circulation launched a cumulative process of subsequent academic mobility and collaboration that contributed significantly to the reintegration of Germany into the international scientific community after the Second World War and enabled the country's rise to the most important source for international co‐authors of US scientists and engineers in the twenty‐first century. In this article I discuss regional and disciplinary specificities in the formation of transnational knowledge networks through circulating academics and suggest that the long‐term effects can be fruitfully conceptualized as accumulation processes in ‘centres of calculation’.     

Researching social care for minority ethnic older people: implications of some Scottish research

Summary The paper explores issues raised by research on patterns ofwelfare and mutual care among older Pakistani people and theirfamilies, with particular reference to their use, or lack ofuse of social care services. Qualitative interviews were conductedwith (self-defined) minority ethnic older people, professionalsin statutory services and in minority-led community-based groups.The organization of services is discussed, with special referenceto issues of power and control, the role of the voluntary (non-profit)and statutory sectors, the debate regarding the possible needfor specialist versus generic services, and the role of minority-ledinitiatives. Secondly, issues of service staffing are examined,and the dangers of marginalizing minority services and staffreviewed, especially with reference to divisions of labour inservice provision and staff's own perspectives. Thirdly, themethodological implications of the work are explores, includingissues of linguistic and cultural communication and workingwith relatively powerless social groups, whose views are sought.Examining the views of professional social care workers is alsoproblematized, with reference to their gate-keeping role. Itis argued that user perspectives are essential to the developmentof appropriate services for minority ethnic groups. In conclusion,the need to raise these issues in other work is discussed.     

The perils with the misuse of predictive power

In early drug development, especially when studying new mechanisms of action or in new disease areas, little is known about the targeted or anticipated treatment effect or variability estimates. Adaptive designs that allow for early stopping but also use interim data to adapt the sample size have been proposed as a practical way of dealing with these uncertainties. Predictive power and conditional power are two commonly mentioned techniques that allow predictions of what will happen at the end of the trial based on the interim data. Decisions about stopping or continuing the trial can then be based on these predictions. However, unless the user of these statistics has a deep understanding of their characteristics important pitfalls may be encountered, especially with the use of predictive power. The aim of this paper is to highlight these potential pitfalls. It is critical that statisticians understand the fundamental differences between predictive power and conditional power as they can have dramatic effects on decision making at the interim stage, especially if used to re-evaluate the sample size. The use of predictive power can lead to much larger sample sizes than either conditional power or standard sample size calculations. One crucial difference is that predictive power takes account of all uncertainty, parts of which are ignored by standard sample size calculations and conditional power. By comparing the characteristics of each of these statistics we highlight important characteristics of predictive power that experimenters need to be aware of when using this approach.