Social Antecedents of Medical Fraud and Over-Servicing: What Makes A Doctor Criminal?

Medical fraud and overservicing are estimated to cost the Australian community between $130 and $200 million per annum, a figure far greater than the national cost of burglary and almost the same as the total property loss from all conventional crime. An examination of the social antecedents of medical fraud and overservicing suggests that the predisposition of some doctors to engage in these practices occurs because of the following: (1) medical training and professional socialization that orientate student doctors away from altruistic health issues towards narrower self-interested professional concerns; (2) career expectations of a high pattern of material consumption that are often frustrated by an increasingly competitive medical market place; and (3) professional medical organizations that lobby for national health policies which reflect the mercenary self-interest of doctors rather than the health interest of the nation.     

Notebooks,B.F. Skinner A Referenced Index of Entries Relevant to Verbal Behavior

The Analysis of Verbal Behavior -     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

论奥斯古德对昆明高峣社区的人类学研究

1938年夏天,美国年轻学者科尼利尔斯·奥斯古德为了履行与燕京大学的合作计划,不远万里来到滇池西岸一个名不见经传的小村庄调查.1961年,他开启尘封的笔记本,整理旧资料,补充新文献,完成了<旧中国的农村生活:对云南高峣的社区研究>.70年过去,重温科尼利尔斯·奥斯古德的研究历程,我们仍会从中获得许多有益的启示.     

Chemical risk communication through the Internet in Spain

In this work, some corporate websites of the chemical industry of Tarragona (Spain) are studied, analysing how the chemical trade associations and companies present information and/or encourage dialogue with the community on issues relating to the chemical risk and their environmental, health and safety (EHS) performance. The results suggest that the chemical industry in Tarragona uses the corporate websites mainly to disseminate information about its EHS commitments and performance, but they do not encourage dialogue with the community through the Internet.     

Adaptive tests of regression functions via multiscale generalized likelihood ratios

Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. She derives the asymptotic joint distribution of the component tests and that of the proposed test under the null hypothesis. She also develops a simple method of selecting the smoothing parameters for the proposed test and presents two approximate methods for obtaining its P‐value. Finally, she evaluates the proposed test through simulations and illustrates its application to a set of real data.     

Social impact Analysis in Australia: Policy Issues for the 1980s

Over the last ten years in Australia the social component of Australian Environmental Impact statements has gradually increased in size as the importance of Social Impact Analysis has been recognised. This commendable trend has not been without mishap, however, and there remain many areas largely ignored in Social Impact Assessment. This paper seeks to identify these areas, identifying value questions that need to be addressed in the preparation of the social impact component.     

Empirical Non-Parametric Control Charts: Estimation Effects and Corrections

Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider non- parametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.