Physician organizations. Part 1: A report from the trenches of a start-up PO

How has Community Health Partners, a physician organization based in Kansas City, turned the corner as it rolls into the second year of operation? The biggest indicator is that CHP hammered out the city's first professional risk contracts and the PO has grown from 23 to more than 50 physician member/owners. Looking back, there are at least 10 reasons why CHP made it this far. These are not reasons you learn about in medical school or an MBA program. There is no one-size-fits-all template for building POs. No fixed organizational chart. No neon signs pointing to the best capital partner. Part I explores five reasons for success, such as having a strong board and physician leadership, as well as educating participating physicians about capitation and affiliating with any hospital or payer that really knows how to partner with physicians. Part 2 will focus on five more lessons learned from the trenches of a start up PO.     

The Determinants of Trust and Credibility in Environmental Risk Communication: An Empirical Study

This study examines a key component of environmental risk communication; trust and credibility. The study was conducted in two parts. In the first part, six hypotheses regarding the perceptions and determinants of trust and credibility were tested against survey data. The hypotheses were supported by the data. The most important hypothesis was that perceptions of trust and credibility are dependent on three factors: perceptions of knowledge and expertise; perceptions of openness and honesty; and perceptions of concern and care. In the second part, models were constructed with perceptions of trust and credibility as the dependent variable. The goal was to examine the data for findings with direct policy implications. One such finding was that defying a negative stereotype is key to improving perceptions of trust and credibility.     

ATSDR Science Panel on the Bioavailability of Mercury in Soils: Lessons Learned

On the basis of discussion and analysis during and following an ATSDR science panel on the bioavailability of mercury in soils, it is apparent that the default assumption of 100% relative bioavailability for mercury-contaminated soils is excessively conservative. However, current knowledge does not allow the development of default assumptions or guidelines for determining relative bioavailability of mercury in soils. Until such default assumptions or guidelines can be developed, site-specific assays of bioavailability, preferably using either animal bioassays or validated in vitro techniques, may provide the best approach for estimating soil-mercury bioavailability.     

A Case Study of Gender Differences in Job Satisfaction Subsequent to Implementation of an Employment Equity Programme

In this research case study, the competing hypothesis of socialization, structural and social role theories were derived and used to explore and compare the job attitudes and satisfaction of women and men in a Canadian insurance company subsequent to voluntary implementation of an employment equity programme. Gender comparisons of both managerial and clerical employees on individual facets of satisfaction indicated the most support for social role theory, some support for structural theory and the least support for socialization theory. These findings indicate that managerial women and men tend to derive work satisfaction from similar sources. The variation in facet results cautions against global conclusions about gender and job satisfaction. Unanticipated consequences such as a 'backlash' (Faludi, 1991) of managerial men and a 'glass escalator' (Williams, 1992) for clerical men were also suggested by the findings in this case. A key implication is the potential benefit to both organizations and managerial women from steps taken to level the structural playing field.     

Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS

This paper reviews a variety of studies in which multi-attribute utility theory (MAU) has been used successfully to aid group decision processes and discusses the incorporation of MAU in Group Decision Support Systems (GDSS). GDSS of various levels of automation have been proposed to aid group decision making. A number of commercial GDSSs are available which are capable of aiding generation of alternatives, their evaluation, and selection. Many authors have promoted the incorporation of quantitative decision making models in GDSS. The most appropriate quantitative tool for GDSS is MAU, but MAU has rarely been utilized. The cases reviewed illustrate that the use of MAU techniques is not prohibitively difficult or complex.     

Inference on effect size indices from several two-armed experiments

The statistical inference problem on effect size indices is addressed using a series of independent two-armed experiments from k arbitrary populations. The effect size parameter simply quantifies the difference between two groups. It is a meaningful index to be used when data are measured on different scales. In the context of bivariate statistical models, we define estimators of the effect size indices and propose large sample testing procedures to test the homogeneity of these indices. The null and non-null distributions of the proposed testing procedures are derived and their performance is evaluated via Monte Carlo simulation. Further, three types of interval estimation of the proposed indices are considered for both combined and uncombined data. Lower and upper confidence limits for the actual effect size indices are obtained and compared via bootstrapping. It is found that the length of the intervals based on the combined effect size estimator are almost half the length of the intervals based on the uncombined effect size estimators. Finally, we illustrate the proposed procedures for hypothesis testing and interval estimation using a real data set.     

Locally optimal designs for the simple death process

We demonstrate the existence and uniqueness of optimal observation schedules for discrete observation of the simple death process. Optimality is framed in the sense of observing the process at times that maximise the Fisher information for the death rate. We examine the relationship of our designs with those obtained by other authors for the simple birth process. A number of interesting properties are uncovered. Practical considerations in the application of these designs in an experimental setting are investigated, and we examine the performance of some approximately optimal designs that are usually simpler to implement. We will show that our optimal designs are highly robust to mis-specification of the death rate.     

An alternate version of the conceptual predictive statistic based on a symmetrized discrepancy measure

The conceptual predictive statistic, C_p, is a widely used criterion for model selection in linear regression. C_p serves as an estimator of a discrepancy, a measure that reflects the disparity between the generating model and a fitted candidate model. This discrepancy, based on scaled squared error loss, is asymmetric: an alternate measure is obtained by reversing the roles of the two models in the definition of the measure. We propose a variant of the C_p statistic based on estimating a symmetrized version of the discrepancy targeted by C_p. We claim that the resulting criterion provides better protection against overfitting than C_p, since the symmetric discrepancy is more sensitive towards detecting overspecification than its asymmetric counterpart. We illustrate our claim by presenting simulation results. Finally, we demonstrate the practical utility of the new criterion by discussing a modeling application based on data collected in a cardiac rehabilitation program at University of Iowa Hospitals and Clinics.