Obesity in America: Implications for Clinical and Health Psychologists

The number of individuals classified as overweight and obese is increasing at alarming rates and these conditions are associated with numerous psychological and physiological health problems. Within this epidemic, there is an increasing need and demand for psychologists and other mental health professionals to participate in the care of patients with obesity, both in assessment and treatment domains. The current paper provides a review of the status of the obesity epidemic, including psychological comorbidities and treatment approaches. Throughout this review, we highlight and discuss the important roles and impact psychologists can have. These roles include serving as members of multidisciplinary obesity treatment programs, conducting assessments for bariatric surgery, and treating related psychiatric disorders, such as depression and disordered eating. Other considerations, such as weight gain associated with psychiatric medications, and directions for future involvement of psychologists in addressing the obesity epidemic are also discussed.     

Integrating Mental Health and Primary Care Services in the Department of Veterans Affairs Health Care System

Integrating mental health care in the primary care setting has been identified in the literature as a model for increasing access to mental health services and has been associated with enhanced clinical and functional patient outcomes and higher patient satisfaction. The Department of Veterans Affairs (VA), which operates the nation’s largest integrated health care system, has taken a leadership role in creating a health care system in which mental health care is provided in the primary care setting. This article examines VA’s efforts and progress to date in implementing evidence-based models of integrated mental health services nationally in community based outpatient clinics, home based primary care, and outpatient primary clinics at medical facilities. Psychology plays an important role in this progress, as part of an overall interdisciplinary effort, in which all professions are crucially important and work together to promote the overall well-being of patients. This article is based in part on a presentation by the first author at the 3rd National Conference of the Association of Psychologists in Academic Health Centers (APAHC) in May of 2007 in Minneapolis, Minnesota.     

Faceted Thesauri

The basic elements of faceted thesauri are described, together with a review of their origins and some prominent examples. Their use in browsing and searching applications is discussed. Faceted thesauri are distinguished from faceted classification schemes, while acknowledging the close similarities. The paper concludes by comparing faceted thesauri and related knowledge organization systems to ontologies and discussing appropriate areas of use.     

Categories and Facets in Integrative Levels

Facets and general categories used in bibliographic classification have been based on a disciplinary organization of knowledge. However, facets and categories of phenomena independent from disciplines can be identified similarly. Phenomena can be classified according to a series of integrative levels (layers), which in turn can be grouped into the major strata of form, matter, life, mind, society and culture, agreeing with Nicolai Hartmann’s ontology. Unlike a layer, a stratum is not constituted of elements of the lower ones; rather, it represents the formal pattern of the lower ones, like the horse hoof represents the shape of the steppe. Bibliographic categories can now be seen in the light of level theory: some categories are truly general, while others only appear at a given level, being the realization of a general category in the specific context of the level: these are the facets of that level. In the notation of the Integrative Level Classification project, categories and facets are represented by digits, and displayed in a Web interface with the help of colours.     

The Acceptability and the Tolerability of Societal Risks: A Capabilities-based Approach

In this paper, we present a Capabilities-based Approach to the acceptability and the tolerability of risks posed by natural and man-made hazards. We argue that judgments about the acceptability and/or tolerability of such risks should be based on an evaluation of the likely societal impact of potential hazards, defined in terms of the expected changes in the capabilities of individuals. Capabilities refer to the functionings, or valuable doings and beings, individuals are able to achieve given available personal, material, and social resources. The likely impact of a hazard on individuals' capabilities should, we argue, be compared against two separate thresholds. The first threshold specifies the minimum level of capabilities attainment that is acceptable in principle for individuals to have in the aftermath of a hazard over any period of time. This threshold captures the level that individuals' capabilities ideally should not fall below. A risk is acceptable if the probability that the attained capabilities will be less than the acceptable level is sufficiently small. In practice, it can be tolerable for some individuals to temporarily fall below the acceptable threshold, provided this situation of lower capabilities attainment is temporary, reversible, and the probability that capabilities will fall below a tolerability threshold is sufficiently small. This second, tolerable threshold delimits an absolute minimum level of capabilities attainment below which no individual in a society should ever fall, regardless of whether that level of capabilities attainment is temporary or reversible. In this paper, we describe and justify this Capabilities-based Approach to the acceptability and tolerability of risks. We argue that the proposed theoretical framework avoids the limitations in current approaches to acceptable risk. The proposed approach focuses the attention of risk analysts directly on what should be our primary concern when judging the acceptability and the tolerability of risks, namely, how risks impact the well-being of individuals in a society. Also, our Capabilities-based Approach offers a transparent, easily communicable way for determining the acceptability and the tolerability of risks.     

A model for explanation of personal attitudes toward traffic of candidate drivers attending drivers’ courses: Risky candidate driver’s attitude model

There exist a good many scientific studies into risk-taking and risky behavior displayed by reckless drivers; however, there are only a few studies into attitudes towards the traffic displayed by candidate drivers. The present study aims to investigate the dimensionality of candidate attitudes’ drivers. Data were collected from questionnaire completed by 258 candidate drivers, and were divided into two sets. The first data was used to explore the underlying factor structure and five latent factors were derived; Factor A Attitude towards drinking and driving; Factor P positive attitude toward traffic; Factor S speeding; Factor T traffic flow vs. rule obedience; and Factor R risky candidate driver’s attitude. The second data set was used to confirm this factorial structure using confirmatory factor analysis. The fit indices showed that the model fitted the data well.     

BEHAVIOR ANALYSIS: THRIVING,BUT HOW ABOUT ITS FUTURE?

Behavior analysis has been thriving by continuing to make important theoretical and empirical contributions to a wide array of problems, as well as by contributing to interdisciplinary research. Applied research in behavior analysis is flourishing. Despite these positive signs there may be an erosion of support for basic research in animal learning and behavior, including behavior analysis. Increased attention by behavior analysts to fundamental problems in areas of cognition, including decision-making and language, may help behavior analysis to evolve more successfully. An experimental analysis of gambling may prove particularly fruitful.     

The Journal of the Experimental Analysis of Behavior at fifty

The Journal of the Experimental Analysis of Behavior was founded in 1958 by a group of male psychologists, mainly from the northeastern USA and connected with either Harvard or Columbia. Fifty years later about 20% of both editors and authors reside outside this country and almost the same proportion is made up of women. Other changes in the journal include having its own website for more than a decade and now publishing online as well as on paper. A recent connection with PubMed Central of the National Library of Medicine has made possible the completely free electronic presentation of the entire archive of about 3,800 articles.     

Was William James a Patient at McLean Hospital for the Mentally Ill?

Rumors that William James was a patient at McLean Asylum near Boston have persisted for several decades. I focus on the reasons why the question has been so difficult to answer in any definitive way; assess the evidence presented in support of the rumors; note that two different periods in James’s life (late twenties and early sixties) have been judged the most likely; and explore the diagnostic question as well: If he was in fact a patient, for what was he being treated? I also discuss evidence that his younger brother Robertson was a patient at McLean and consider the bearing of this evidence on the question of whether William James was a patient at McLean and on the diagnostic issue.     

Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with finite mixture models performed the worst.