Making sense of loss: a content analysis of end-of-life practitioners' therapeutic approaches

Clinical professionals working in end-of-life (EOL) contexts are frequently relied upon to address questions of meaning with dying and bereaved persons. Similar to the gulf between researchers and practitioners besetting the larger healthcare community, the voices of EOL practitioners are often underrepresented in the empirical literature. This study aimed to further the dialogue in the field of thanatology by surveying and describing the therapeutic approaches that EOL practitioners most commonly report using to facilitate meaning-making. A total of 119 practitioners from a range of EOL disciplines were surveyed to write about their intervention strategies for helping clients/patients make sense of loss. Overall, participants discussed using 23 different therapeutic approaches that comprised three overarching categories: 1) presence of the helping professional; 2) elements of the process; and 3) therapeutic procedures. Importantly, the results also indicated that practitioners from the different EOL occupations are converging on many of the same strategies for promoting meaning-making. Implications for future research on evaluating the effectiveness of meaning-making interventions are also discussed.     

A new tool for hospital/ physician strategic alignment: the subsidiary physician corporation

Physicians' and hospitals' strategies often diverge--with poor results for both. The causes of this divergence are many. They include different methods of payment, divisive governmental regulations (like Stark) and cultural differences.     

Letters to the Editor

Although several authors have indicated that the median test has low power in small samples, it continues to be presented in many statistical textbooks, included in a number of popular statistical software packages, and used in a variety of application areas. We present results of a power simulation study that shows that the median test has noticeably lower power, even for the double exponential distribution for which it is asymptotically most powerful, than other readily available rank tests. We suggest that the median test be “retired” from routine use and recommend alternative rank tests that have superior power over a relatively large family of symmetric distributions.     

A comparison of methods for studentizing the sample median

Various methods for “Studentizing” the sample median are com-pared on the basis of a Monte Carlo study. Several of the methods do rather poorly while two, the bootstrap and the standardized length of a distribution free confidence interval, behave accept-ably acrors a wide range of sample sizes and several distributions of varying tail length. These two methods seem to agree closely with the distribution free confidence intervals and moreover, un-like these intervals, the methods can be extended to a method of accurate inference for λ₁ regreasion.     

A comparison of estimation techniques for the three parameter pareto distribution

This paper compares minimum distance estimation with best linear unbiased estimation to determine which technique provides the most accurate estimates for location and scale parameters as applied to the three parameter Pareto distribution. Two minimum distance estimators are developed for each of the three distance measures used (Kolmogorov, Cramer‐von Mises, and Anderson‐Darling) resulting in six new estimators. For a given sample size 6 or 18 and shape parameter 1(1)4, the location and scale parameters are estimated. A Monte Carlo technique is used to generate the sample sets. The best linear unbiased estimator and the six minimum distance estimators provide parameter estimates based on each sample set. These estimates are compared using mean square error as the evaluation tool. Results show that the best linear unbaised estimator provided more accurate estimates of location and scale than did the minimum estimators tested.     

Bayesian Transformation Models for Multivariate Survival Data

In this paper, we propose a general class of Gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported.