Correlates of honor ratings in a clinical clerkship employing a faculty forum evaluation system

5he purpose of this study was to determine whether or not suspected variables affected a surgery clerk's chances of being awarded an honor rating. Findings indicated a significant relationship between a student receiving an honor rating and his or her preceptor's predetermined level of student advocacy, the number of completed patient interview and physical examination write-ups, and final examination scores. There was no significant relationship found between honor ratings and the preceptor's status, the sequence in which the student was discussed or the length of time spent discussing the student at the final evaluation meeting, or the number of clinical faculty present at the meeting. These results lend support to the faculty forum evaluation approach, but suggest a need for further scrutiny of some influencing variables to ensure all students are fairly considered and honor ratings judiciously awarded.     

Evaluating a private foundation's health program

Public policy decisions in health are increasingly difficult and expensive. Although there will never be enough information available, private foundations can help to bridge the most important gaps in knowledge. Larger foundations may also wish to respond to those who doubt the value of foundation activities. This article reviews the experiences over the past eight years of The Robert Wood Johnson Foundation in employing evaluation and related social research procedures in the planning and implementation of a major philanthropic effort to improve the health and medical care of Americans. Discussed are the still evolving Foundation evaluation framework, the unanticipated problems in undertaking specific evaluations, and the substantive findings of some of the studies.     

Network analysis in evaluation: some words of caution

Network analysis, a methodology derived from general systems' theory, can be utilized as a community mental health administrative-evaluation procedure. Evaluation parameters derived from analysis of patient data as they “flow” through a network of agency services, provide measures of systemic functioning. These parameters include “longest paths” and various ratio relationships as evaluation measures of service delivery. The limitations of network analysis are examined by means of conceptual analyses, and phenomena that emerged from research experience. The necessity for both quantitative and qualitative data to ensure a meaningful evaluation of mental health services is explained. Conclusions about the value of the network analysis approach are considered.     

The validity of indices for rural health manpower needs assessment

Population-to-practitioner ratios have long been the primary index in the designation of health manpower shortage areas. This paper documents that application of the widely used population-to-dentist index results in understatement of the need for dental health manpower in rural areas. Through the analysis of utilization data collected from a statewide health screening program in Colorado, the practice of sole reliance on the population-to-dentist indices as an indicator of need was tested. Another measure, the area-(square miles) to-dentist ratio was formulated, examined, and found to be a more useful referent of the need for additional health manpower in rural areas. Utilization of dental services in sparsely settled rural counties of Colorado was unrelated to population-to-dentist ratios. A strong, statistically significant association of utilization with land area-to-dentist ratios was found. The findings of this analysis suggest a need for reevaluation of needs assessment methodologies used in the designation of health manpower shortage areas. Indices more sensitive to consumer circumstance than to the number of health care providers available must be considered.     

z Test for the significance of the mean of a stable probability distribution with 1<α≤2

The analysis of data using a stable probability distribution with tail parameter α<2 (sometimes called a Pareto–Levy distribution) seems to have been avoided in the past in part because of the lack of a significance test for the mean, even though it appears to be the correct distribution to use for describing returns in the financial markets. A z test for the significance of the mean of a stable distribution with tail parameter 1<α≤2 is defined. Tables are calculated and displayed for the 5% and 1% significance levels for a range of tail and skew parameters α and β. Through the use of maximum likelihood estimates, the test becomes a practical tool even when α and β are not that accurately determined. As an example, the z test is applied to the daily closing prices for the Dow Jones Industrial average from 2 January 1940 to 19 March 2010.     

Parameter estimation of nonlinear mixed-effects models using first-order conditional linearization and the EM algorithm

Nonlinear mixed-effects (NLME) models are flexible enough to handle repeated-measures data from various disciplines. In this article, we propose both maximum-likelihood and restricted maximum-likelihood estimations of NLME models using first-order conditional expansion (FOCE) and the expectation–maximization (EM) algorithm. The FOCE-EM algorithm implemented in the ForStat procedure SNLME is compared with the Lindstrom and Bates (LB) algorithm implemented in both the SAS macro NLINMIX and the S-Plus/R function nlme in terms of computational efficiency and statistical properties. Two realworld data sets an orange tree data set and a Chinese fir (Cunninghamia lanceolata) data set, and a simulated data set were used for evaluation. FOCE-EM converged for all mixed models derived from the base model in the two realworld cases, while LB did not, especially for the models in which random effects are simultaneously considered in several parameters to account for between-subject variation. However, both algorithms had identical estimated parameters and fit statistics for the converged models. We therefore recommend using FOCE-EM in NLME models, particularly when convergence is a concern in model selection.     

Improving TAIEX forecasting using fuzzy time series with Box–Cox power transformation

Box–Cox together with our newly proposed transformation were implemented in three different real world empirical problems to alleviate noisy and the volatility effect of them. Consequently, a new domain was constructed. Subsequently, universe of discourse for transformed data was established and an approach for calculating effective length of the intervals was then proposed. Considering the steps above, the initial forecasts were performed using frequently used fuzzy time series (FTS) methods on transformed data. Final forecasts were retrieved from initial forecasted values by proper inverse operation. Comparisons of the results demonstrate that the proposed method produced more accurate forecasts compared with existing FTS on original data.     

Power-divergence-type measure of departure from diagonals-parameter symmetry for square contingency tables with ordered categories

For the analysis of square contingency tables with ordered categories, Goodman considered the diagonals-parameter symmetry (DPS) model. This paper proposes a measure to represent the degree of departure from the DPS model. The proposed measure is expressed by applying Read and Cressie’s power-divergence or Patil and Taillie’s diversity index. The measure would be useful for comparing the degree of departure from the DPS model in several tables. Examples are given.     

Parameter selection in modified histogram estimates

A multivariate modified histogram density estimate depending on a reference density g and a partition P has been proved to have good consistency properties according to several information theoretic criteria. Given an i.i.d. sample, we show how to select automatically both g and P so that the expected L ₁ error of the corresponding selected estimate is within a given constant multiple of the best possible error plus an additive term which tends to zero under mild assumptions. Our method is inspired by the combinatorial tools developed by Devroye and Lugosi [Devroye, L. and Lugosi, G., 2001, Combinatorial Methods in Density Estimation (New York, NY: Springer–Verlag)] and it includes a wide range of reference density and partition models. Results of simulations are also presented.