The physician exodus from hospitals

Physicians are spending increasingly less of their work week in the hospital. This is true of surgeons because they are performing more ambulatory surgery, often off the hospital premises, and for primary care physicians because they are delegating hospital care of their patients to others. What are the effects of this physician exodus on hospitals, patients, physicians, and medical education? Some of these consequences are explored, from disruptions in the continuity of care, to increase in practice productivity, to preparing undergraduates for the realities of medical practice.     

Gender comparisons in weight training for collegiate sports

This paper examines one of the factors popularly believed to have discouraged young women from participating in collegiate sports: the fear that their physiques may become unattractive due to the regimen of physical training, such as weight lifting, required in such sports. Based on a study of all the athletes at a private university in the western USA, we found that in comparison with males, female athletes did express greater concern. However, in spite of that fear, they are more rigorous than their male counterparts in weight training. Physical training may have different meanings for the two sexes. Regression analyses, supplemented with qualitative data, show that, for the women, the most important motivation for physical training appears to be the enjoyment that they receive from having a strong body. Among the male athletes, the closest to being a significant factor is that they view weight training as a means to attain a masculine image. It appears that the psychological pathways to physicality for male and female may be quite different. They run against the traditional directions on instrumental and expressive orientations. In addition to the sociology of sport, he has been researching the subculture of the elderly in retirement communities. He has published articles on aging, sexuality, and popular culture. His interests include social learning, behavior management, and psychological assessment.     

A Latent Class Analysis of Factors Associated with Levels of Aggression among Low-Income African American Youth in Chicago

Abstract

African American youth are often stereotyped as being more aggressive than and experience more severe negative social sanctions relative to their European American peers. This cross-sectional study assessed self-reported levels of aggression among a sample of 538 low-income African American youth in Chicago, and employed latent class analysis to determine groups of aggression. Major study findings showed low, middle high aggression group. The largest number of youth fell within the low aggression group. Social factors (e.g., low student-teacher connectedness, high risky peer norms, neighborhood violence, and family conflict) were associated with inclusion in the high aggression group. Intrapersonal factors (e.g., depression, anxiety, post-traumatic stress disorder) were associated with inclusion in the middle aggression group. Future longitudinal research should examine what factors are associated with movement across aggression groups and capitalize upon knowledge gained in the current study to develop interventions.     

Objective and Subjective Status Inconsistency: a Search for Empirical Correspondence

The relationship between objective (researcher) and subjective (respondent) definitions of status inconsistency is examined with data from a survey study of Gary, Indiana. The analysis of a variety of measures of income-occupation inconsistency indicated a moderately high correlation between "normatively" defined and objectively defined inconsistency but otherwise very little relationship between subjective and objective measures. The overall pattern of correlations among the particular measures employed suggests that researchers and subjects share a common mode of status evaluations up to, but not including, the concept of status inconsistency.     

The Development of the Good Relations Measurement Framework in Britain: A Template for Experiential Social Measurement

This article explores the development of the Good Relations Measurement Framework (GRMF), the first attempt in Britain to create a framework designed to measure how people experience their lives, specifically in relation to their interactions with each other. It provides a reference point for others seeking to construct social indicator measurement frameworks which capture the experiential in the social policy field. In a wider sense, it provides a case study of the use of social indicators within the policy process in the modern polity. The overall objective of the GRMF is to measure the state of Good Relations in Britain. Seven key areas emerged as being crucial for the development of measurement frameworks during the construction of the GRMF. Firstly, a decision has to be taken about the extent to which social measurement frameworks are confined to measurement only or are to have a normative element. Secondly, a working definition of the subject area is needed early in the process. Thirdly, an element of consultation with the public is important. A fourth issue relates to the practical method of construction through the use of ‘long lists’ of potential indicators, and finding a balance between an ‘ideal’ list of potential indicators emerging from public consultation and a second list of existing indicators drawn from existing surveys. A fifth issue relates to the availability of social indicator data at an appropriate geographical level. A sixth issue is that social indicators drawn from different surveys are not always comparable. A final factor is that while quantitative indicators are useful as a tool of social measurement, qualitative research adds a further dimension which is especially important in particular circumstances.     

Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway‐Maxwell Poisson (COM‐Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM‐Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM‐Poisson GLM, and (2) estimate the prediction accuracy of the COM‐Poisson GLM using simulated data sets. The results of the study indicate that the COM‐Poisson GLM is flexible enough to model under‐, equi‐, and overdispersed data sets with different sample mean values. The results also show that the COM‐Poisson GLM yields accurate parameter estimates. The COM‐Poisson GLM provides a promising and flexible approach for performing count data regression.     

Part I––Comparing Noncancer Chronic Human Health Reference Values: An Analysis of Science Policy Choices

The goal of this study was to systematically evaluate the choices made in deriving a chronic oral noncancer human health reference value (HHRV) for a given chemical by different organizations, specifically those from the U.S. Environmental Protection Agency, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This analysis presents a methodological approach for comparing both the HHRVs and the specific choices made in the process of deriving an HHRV across these organizations. Overall, across the 96 unique chemicals and 171 two‐way organizational comparisons, the HHRV agreed approximately 26% of the time. A qualitative method for identifying the primary factors influencing these HHRV differences was also developed, using arrays of HHRVs across organizations for the same chemical. The primary factors identified were disagreement on the critical or principal study and differential application of the total uncertainty factor across organizations. Of the cases where the total UF was the primary factor influencing HHRV disagreement, the database UF had the greatest influence.     

Part II: Quantitative Evaluation of Choices Used in Setting Noncancer Chronic Human Health Reference Values Across Organizations

Environmental and public health organizations, including the World Health Organization (WHO) and the U.S. Environmental Protection Agency (USEPA), develop human health reference values (HHRV) that set “safe” levels of exposure to noncarcinogens. Here, we systematically analyze chronic HHRVs from four organizations: USEPA, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This study is an extension of our earlier work and both closely examines the choices made in setting HHRVs and presents a quantitative method for identifying the primary factors influencing HHRV agreement or disagreement.⁽¹⁾ We evaluated 171 organizational comparisons, developing a quantitative method for identifying the factors to which HHRV agreement (that is, when both organizations considering the same data set the identical HHRV values) is most sensitive. To conduct this analysis, a Bayesian belief network was built using expert judgment, including the specific science policy choices analysis made in the context of setting an HHRV. Based on a sensitivity of findings analysis, HHRV agreement is most sensitive to the point of departure value, followed by the total uncertainty factor (UF), critical study, critical effect, animal model, and point of departure approach. This analysis also considered the specific impacts of individual UFs, with the database UF and the subchronic‐to‐chronic UF being identified as primary factors impacting the total UF differences observed across organizations. The sensitivity of findings analysis results were strengthened and confirmed by frequency analyses evaluating which choices most often disagreed when the HHRV and the total UF disagreed.