University students' knowledge of alcoholic drinks and their perception of alcohol-related harm

A total of 371 university students were asked to estimate the amount of alcohol contained in a standard drink and to estimate the number of standard drinks contained in popular alcoholic beverages. In addition, students completed questionnaires assessing their perception of short and long term harm related to the consumption of beer, wine, spirits and pre-mixed alcopops. Results revealed that students were generally inaccurate in their estimate of alcoholic content of beverages, and national guidelines for low risk drinking. Students were also found to hold different perceptions regarding how harmful different alcoholic beverages were. While both male and female students considered spirits to be more harmful than beer, wine and pre-mixed drinks, males also believed beer to be more harmful than wine when considering the short term consequences. The pattern of beliefs reported by this sample suggest a high-risk population who are not aware of the risks they are exposing themselves to through their drinking behavior.     

Disseminating research results to kids: practical tips from the Neighbourhoods for Active Kids study

ABSTRACT

Appropriately disseminating results to children are important because it respects the role they played in the research process. Clearly conveying complex messages to children, however, can be challenging and take a substantial amount of time for researchers. This paper reports on the results dissemination processes and accompanying critical reflection, that occurred during Neighbourhoods for Active Kids study – a community-based health research project with 1102 children aged 8–13 years residing in Auckland, Aotearoa/New Zealand. The results dissemination items included: an individual physical activity and food-purchasing behaviour summary, school physical activity and food-purchasing behaviour summary, colouring-in poster, a video, comic, results booklet, school summary report and school data. The results from critical reflection have been developed into five tips for disseminating results to children. The tips are: consult with children and incorporate their feedback throughout; allow space for change and adaptation; meet children where they are at in the digital world and in the physical world; it’s OK to ask for help; and be careful of the words you use. Findings can be used to inform results dissemination activities across a range of social science disciplines.     

Evidence-based practice in real-world services for young people with complex needs: New opportunities suggested by recent implementation science

There is growing agreement that child and youth services have been slow to implement evidence-based practice (EBP). A variety of interrelated barriers and facilitators of EBP implementation have been recognized and investigated. Key categories include the attitudes of providers, the characteristics of client populations, the characteristics of usual practice, organizational factors, and resource availability. Implementation scientists are investigating these factors, seeking to bridge the gap between clinical science and practice. Much of this work has been conducted in the context of behavioral health services for children and young people. This paper provides a purposeful review of this research from the perspective of decision-makers in agencies serving young people with multiple and complex psychosocial needs. It is argued that major barriers to EBP may be mitigated if ways can be found to transcend the oppositional construction that has characterized the discourses of EBP versus practice wisdom. A conceptual approach to such integration is offered. New lines of implementation research are offering opportunities for practical action by decision-makers consistent with this integrative approach.     

Choices and life chances: feminism and the politics of generational change

The perception that young women are disengaged from feminist politics has provoked a great deal of tension between feminist generations. Recent feminist research into generational change has largely avoided this tension by focusing on the shifting meanings of feminism and the discrepancy between young women's reluctance to identify as “feminists” and their general acceptance of feminist attitudes toward gender issues. Nevertheless, in an era when gender equity goals seem to be if not slipping backwards then lacking urgency, young women are less likely to identify with a collective feminist politics than are older women. Underpinned by the findings of a major study of the attitudes toward work, family, and retirement of three generations of Australian women, this paper develops an approach that helps explain this reluctance. Drawing on the work of Karl Mannheim, the paper suggests that the cultural currents shaping the consciousness of different generations of women impact significantly on gender identity. The implications of this cultural shift are considered in the context of feminist politics and the contemporary “culture wars.”     

Compensating wage differentials for workplace accidents: Evidence for union and nonunion workers in the UK

This article measures compensating wage differentials for job risks for union and nonunion workers. Job risk is made endogenous to avoid a selectivity bias arising if more able people choose safer jobs. We find that this adjustment has a considerable effect on the union group, raising their fatal risk premium above that of non union workers. This implies that there is more variation in unmeasured ability in the unionized group, and that job risk is an inferior good. The fact that unionized workers are also found in safer jobs might therefore be attributable to their greater wealth, rather than to greater“knowledge” in the unionized plant. The estimated statistical value of a life is £8.8 million in 1990 prices for union workers, with nonunion workers about 20% lower.     

Strategies for Fitting Large,Geostatistical Data in MCMC Simulation

Models for geostatistical data introduce spatial dependence in the covariance matrix of location-specific random effects. This is usually defined to be a parametric function of the distances between locations. Bayesian formulations of such models overcome asymptotic inference and estimation problems involved in maximum likelihood-based approaches and can be fitted using Markov chain Monte Carlo (MCMC) simulation. The MCMC implementation, however, requires repeated inversions of the covariance matrix which makes the problem computationally intensive, especially for large number of locations. In the present work, we propose to convert the spatial covariance matrix to a sparse matrix and compare a number of numerical algorithms especially suited within the MCMC framework in order to accelerate large matrix inversion. The algorithms are assessed empirically on simulated datasets of different size and sparsity. We conclude that the band solver applied after ordering the distance matrix reduces the computational time in inverting covariance matrices substantially.     

A BAYESIAN SIMULATION APPROACH TO INFERENCE ON A MULTI‐STATE LATENT FACTOR INTENSITY MODEL

The influence of economic conditions on the movement of a variable between states (for example a change in credit rating from A to B) can be modelled using a multi‐state latent factor intensity framework. Estimation of this type of model is, however, not straightforward, as transition probabilities are involved and the model contains a few highly analytically intractable distributions. In this paper, a Bayesian approach is adopted to manage the distributions. The innovation in the sampling algorithm used to obtain the posterior distributions of the model parameters includes a particle filter step and a Metropolis–Hastings step within a Gibbs sampler. The feasibility and accuracy of the proposed sampling algorithm is supported with a few simulated examples. The paper contains an application concerning what caused 1049 firms to change their credit ratings over a span of ten years.     

Non-stationary partition modeling of geostatistical data for malaria risk mapping

The most common assumption in geostatistical modeling of malaria is stationarity, that is spatial correlation is a function of the separation vector between locations. However, local factors (environmental or human-related activities) may influence geographical dependence in malaria transmission differently at different locations, introducing non-stationarity. Ignoring this characteristic in malaria spatial modeling may lead to inaccurate estimates of the standard errors for both the covariate effects and the predictions. In this paper, a model based on random Voronoi tessellation that takes into account non-stationarity was developed. In particular, the spatial domain was partitioned into sub-regions (tiles), a stationary spatial process was assumed within each tile and between-tile correlation was taken into account. The number and configuration of the sub-regions are treated as random parameters in the model and inference is made using reversible jump Markov chain Monte Carlo simulation. This methodology was applied to analyze malaria survey data from Mali and to produce a country-level smooth map of malaria risk.