The gambling lifestyle: I. theory

This paper describes the lifestyle model of gambling behavior in which compulsive gambling behavior is conceptualized as a lifestyle characterized by pseudoresponsibility, self-ascension, hypercompetitiveness, and social rule breaking/bending/ twisting. The underlying premise of this theory is that gambling behavior takes on the appearance of a lifestyle when it is viewed by the individual as a viable means of enhancing self-worth, minimizing personal insecurity, and controlling fear. Three primary areas of theoretical and research interest are covered in this paper: (1) the theoretical underpinnings of lifestyle theory; (2) the individual components (conditions, choice, cognition) of a gambling lifestyle; and (3) the developmental progression of a gambling lifestyle.The author would like to thank Henry Lesieur and anonymous reviewers of earlier versions of this paper for their helpful comments. The assertions and opinions contained herein are the private views of the author and should not be construed as official or as reflecting the views of the Federal Bureau of Prisons or United States Department of Justice.     

Making the Best of a Bad Job? Female Part‐Timers’ Orientations and Attitudes to Work

This article uses data from 50 qualitative interviews with female part‐time workers in low‐level jobs in Britain to illustrate their attitudes, orientations and aspirations towards paid work. The research reports two main findings. Firstly, even female part‐time workers at the bottom end of the occupational structure are not a homogeneous group of workers. Secondly, they are not all highly satisfied with their jobs. The article argues that many women (especially those in working‐class households) still do not have a genuine choice between ‘family work’ and ‘market work’. It concludes by presenting a theoretical model of orientations to work.     

Teaching Children With Autism Spectrum Disorder to Report Past Behavior With the Use of a Speech-Generating Device

The Analysis of Verbal Behavior -     

Graphical Presentations of Data Envelopment Analyses: Management Implications from Parallel Axes Representations*

The conversion of multivariate data analysis into information that managers can readily use in their decision making has always been a challenging task. In this paper we provide a parallel axis representation of multivariate data and demonstrate how this representation may be used as a tool for implementing the results of data envelopment analysis. We use an analysis of the efficiency of electricity generating plants to illustrate the technique.     

‘Before they kill my spirit entirely’: insights into the lived experiences of American Indian Alaska Native faculty at research universities

American Indian and Alaska Natives (AIAN) comprise about 2% of the US population and 0.5% of the faculty in higher education. While scholars have documented the experiences of underrepresented minority (URM) faculty, the perspectives of AIAN faculty at elite universities are largely absent. Although AIAN faculty share many of the same barriers to success as URM colleagues, their unique status as Tribal peoples and their relationship to settler colonialism pose particular challenges and resistance strategies. Findings from a mixed methods study of 25 AIAN faculty at research universities, employing a decolonizing, survivance-oriented framework, examines their lived institutional experiences. The data yielded five themes regarding (1) institutional climate, (2) mentorship, (3) family–work balance, (4) cultural taxation and role stress, and (5) discrimination. The authors conclude that decolonization involves the repatriation of Indigenous epistemologies and Indigenous ‘place’ in the growth of science, research, and knowledge production, creating liberatory spaces within the academy.     

Professional quality of life of adult protective service workers

Adult Protective Services (APS) workers are exposed to substantial occupational hazards and job stress, but these stressors are underdocumented. Therefore, we sought to describe APS workers’ work environments and responses to occupational hazards and stressors, including compassion fatigue, burnout, and secondary traumatic stress. Survey data were gathered with closed-ended questionnaires administered to APS workers in an urban setting. Virtually all workers (97%) reported exposure to one or more environmental hazards in their work, and 80% reported hazard exposure in the past month. Workers also reported mixed responses to their work environment and to experiences with supervision. A sizable minority (22.7%) was at high risk for burnout, 24.6% were at risk for secondary traumatic stress, and 19.9% reported low compassion satisfaction. The results document multiple stressors in APS work. The APS partner is committed to ongoing efforts to better support its staff, and these findings can inform future efforts to enhance supervisor support and worker self-care, to minimize burnout and secondary traumatic stress.     

ASSOCIATIONS BETWEEN AIR POLLUTION AND HOSPITAL VISITS FOR CARDIOVASCULAR DISEASES IN THE ELDERLY IN SYDNEY USING BAYESIAN STATISTICAL METHODS

Using generalized linear models (GLMs), Jalaludin  et al. (2006;  J. Exposure Analysis and Epidemiology   16 , 225–237) studied the association between the daily number of visits to emergency departments for cardiovascular disease by the elderly (65+) and five measures of ambient air pollution. Bayesian methods provide an alternative approach to classical time series modelling and are starting to be more widely used. This paper considers Bayesian methods using the dataset used by Jalaludin  et al.  (2006) , and compares the results from Bayesian methods with those obtained by Jalaludin  et al.  (2006) using GLM methods.     

Consultants' forum: should post hoc sample size calculations be done?

Pre‐study sample size calculations for clinical trial research protocols are now mandatory. When an investigator is designing a study to compare the outcomes of an intervention, an essential step is the calculation of sample sizes that will allow a reasonable chance (power) of detecting a pre‐determined difference (effect size) in the outcome variable, at a given level of statistical significance. Frequently studies will recruit fewer patients than the initial pre‐study sample size calculation suggested. Investigators are faced with the fact that their study may be inadequately powered to detect the pre‐specified treatment effect and the statistical analysis of the collected outcome data may or may not report a statistically significant result. If the data produces a “non‐statistically significant result” then investigators are frequently tempted to ask the question “Given the actual final study size, what is the power of the study, now, to detect a treatment effect or difference?” The aim of this article is to debate whether or not it is desirable to answer this question and to undertake a power calculation, after the data have been collected and analysed. Copyright © 2008 John Wiley & Sons, Ltd.     

Non‐accidental injury and bone fragility disorders: the need for a multidisciplinary perspective

A case is presented of a child who was initially diagnosed with osteogenesis imperfecta following a skull fracture. However, she later presented with multiple fractures and soft tissue injuries which were considered to be due to non‐accidental injury. It can be perplexing distinguishing accidental and non‐accidental injury in children where a bone fragility disorder has been suspected. A combination of a careful review of the history, a thorough examination and expert review of the radiology is imperative. DNA testing can be helpful. Multidisciplinary case review, taking into account the social history and any concerns professionals have about the family, is invaluable in informing a difficult diagnosis. In this context, there is a definite role for other agencies in challenging the diagnosis or exclusion of non‐accidental injury. Professionals should be willing to challenge the exclusion of non‐accidental injury when such exclusion is not evidence based. Copyright © 2009 John Wiley & Sons, Ltd.