Risk Factors for Problem Gambling Among Indigenous Australians: An Empirical Study

Despite a long history of gambling amongst many Indigenous peoples, knowledge about contemporary Indigenous gambling is sparse. In Australia, previous studies of Indigenous gambling have been severely limited in number, scope and rigour. The research reported in this paper is based on the first Indigenous-specific quantitative gambling research undertaken in Australia since 1996 and draws on the largest sample to date. This study examined numerous aspects of gambling among Indigenous Australians. After appropriate consultations and permission, the study collected surveys from 1,259 self-selected Indigenous adults in 2011 at three Indigenous festivals, online and in several Indigenous communities. This paper draws on these data to identify problem gambling risk factors by comparing selected socio-demographic characteristics, early exposure to gambling, gambling motivations, gambling behaviour, gambling cognitions, and substance use while gambling, amongst non-problem, low risk, moderate risk and problem gamblers. A logistic regression investigated the difference between problem gamblers and all other PGSI groups. Risk factors associated with being a problem gambler were: being older, commencing gambling when under 10 years old, always being exposed to adults gambling as a child, using alcohol and/or drugs while gambling, having family and friends who gamble, having an addiction to gambling and not gambling to socialise, having a high expenditure on commercial gambling, and living in a state or territory other than NSW or QLD. Public health measures to address these risk factors are identified.     

Emergency departments and street-involved youth: factors influencing utilization

Homeless youth often have untreated health problems and experience barriers to care. Existing evidence suggests that homeless youth use emergency departments (EDs) more frequently than other youth. We hypothesized that street youth with access to primary care services would be less likely to utilize EDs for health care. Data were drawn from a study of housing security and changes to it over time among 150 youth (16–20 years) recruited from service agencies for street-involved youth. Our findings show that those with access to primary care were actually more likely to use the ED, suggesting that access may encourage further engagement with the health care system. Findings support the need to better understand why access to primary care increases ED use and what could be done to change this pattern.     

Aesthetic Labor for the Sociologies of Work,Gender, and Beauty

Amid the growing literature on the costs and rewards of physical appearance for labor market outcomes, an economistic emphasis on looks as an investment strategy has gained prominence. The concept of aesthetic labor is a useful sociological intervention for understanding how the value of certain looks is constructed, and how looks matter for social stratification. Aesthetic labor is the practice of screening, managing, and controlling workers on the basis of their physical appearance. The concept advances research on the service economy by moving beyond a focus on emotions to emphasize worker corporeality. This article first untangles aesthetic labor from related concepts, including body work, emotional labor, and embodied cultural capital. Next is a review of three contexts in which scholars have applied aesthetic labor to the workplace: the organization, freelance labor, and the market. Because it situates the value of beauty in context, aesthetic labor foregrounds those power relations that define aesthetics, such as class, race, and gender. The concept incorporates insights from field theories of bodily capital, such that aesthetic labor denaturalizes beauty and seeks to explain the processes through which looks translate into economic and symbolic rewards.     

Fitting models for the joint action of two drugs using SAS

Combinations of drugs are increasingly being used for a wide variety of diseases and conditions. A pre-clinical study may allow the investigation of the response at a large number of dose combinations. In determining the response to a drug combination, interest may lie in seeking evidence of 'synergism', in which the joint action is greater than the actions of the individual drugs, or of 'antagonism', in which it is less. Two well-known response surface models representing no interaction are Loewe additivity and Bliss independence, and Loewe or Bliss synergism or antagonism is defined relative to these. We illustrate an approach to fitting these models for the case in which the marginal single drug dose-response relationships are represented by four-parameter logistic curves with common upper and lower limits, and where the response variable is normally distributed with a common variance about the dose-response curve. When the dose-response curves are not parallel, the relative potency of the two drugs varies according to the magnitude of the desired effect and the models for Loewe additivity and synergism/antagonism cannot be explicitly expressed. We present an iterative approach to fitting these models without the assumption of parallel dose-response curves. A goodness-of-fit test based on residuals is also described. Implementation using the SAS NLIN procedure is illustrated using data from a pre-clinical study.     

Evaluating the performance of sparse principal component analysis methods in high-dimensional data scenarios

High-dimensional datasets have exploded into many fields of research, challenging our interpretation of the classic dimension reduction technique, Principal Component Analysis (PCA). Recently proposed Sparse PCA methods offer useful insight into understanding complex data structures. This article compares three Sparse PCA methods through extensive simulations, with the aim of providing guidelines as to which method to choose under a variety of data structures, as dictated by the variance-covariance matrix. A real gene expression dataset is used to illustrate an application of Sparse PCA in practice and show how to link simulation results with real-world problems.