Unwarranted variations in the quality of health care: can the law help medicine provide a remedy/remedies?

This Article reviews the essential findings of studies of variations in quality of care according to three categories of care: effective care, preference-sensitive care, and supply-sensitive care. It argues that malpractice liability and informed consent laws should be based on standards of practice that are appropriate to each category of care. In the case of effective care, the legal standard should be that virtually all of those in need should receive the treatment, whether or not it is currently customary to provide it. In the case of preference-sensitive care, the law should recognize the failure of the doctrine of informed consent to assure that patient preferences are respected in choice of treatment; we suggest that the law adopt a standard of informed patient choice in which patients are invited, not merely to consent to a recommended treatment, but to choose the treatment that best advances their preferences. In the case of supply-sensitive care, we suggest that physicians who seek to adopt more conservative patterns of practice be protected under the "respectable minority" or "two schools of thought" doctrine.     

From evaluation to auditing and from programs to institutions: Causes and consequences of the decline of the program approach

In the wake of the “audit revolution” during the 1980s and 1990s, administrative systems across the western world have tended to gradually downplay evaluation as a component of the policy process. At the same time, auditing has dramatically increased in scope and depth, and has also, to some extent, come to fill the role in policymaking previously played by evaluation studies. This article reviews this development from the perspective of policymaking and institutional change or “drift.” Specifically, we look at the types of information and knowledge that are lost and gained as a result of these developments. We apply a cross‐national approach, drawing on the examples from the European and North American contexts.     

School Adjustment in Rural and Urban Communities: Do Students from “Timbuktu” Differ from Their “City Slicker” Peers?

The primary purpose of our study was to explore the effects of rurality on school adjustment and other school-related variables. Using data from 167,738 7th–12th graders located in a national sample of 185 predominantly white communities, multilevel models were estimated for perceived school performance and school liking using a variety of individual-level (e.g., gender, ethnicity, and peer school performance) and community/school-level variables (e.g., school size, rurality, and percentage free/reduced lunch) as predictor variables. Rurality was not significantly related to school adjustment, but rather, the characteristics of individuals living within those communities were. Results also indicated that participation in school and non-school activities, a strength of rural schools, can play a positive role in school adjustment. Given the significant relationships of income and parental education to all of the school-related variables, a key long-term strategy may lie in improving the economic climate of rural areas.

Microscopic Saw Mark Analysis: An Empirical Approach

Microscopic saw mark analysis is a well published and generally accepted qualitative analytical method. However, little research has focused on identifying and mitigating potential sources of error associated with the method. The presented study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw mark analysis. The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62–17.82%.