“Sensory Ordering” in Nurses' Clinical Decision‐Making: Making Visible Senses,Sensing, and “Sensory Work” in the Hospital

The objective of this article is to present a study on the constitutive role of senses in clinical decision‐making. The methodology is based on a series of focus groups with nurses in various hospital departments. Based on a narrative approach, our study examines “sensory work” in clinical decision‐making in order to reveal its specificity in the clinical work of nurses. Nurses shared stories—in focus groups—about the influence of senses in clinical decision‐making. The analysis of clinical narratives helped to identify various situations revealing the “sensory work” that underlines clinical decision‐making. We put the emphasis on the spectrum of sensory activities and the interactions occurring during a clinical decision‐making. One specific contribution of our study is to make visible the “sensory ordering” at work as constituted by interactions between nurses during a clinical assessment.     

Heterogeneity and varying effect in hazards regression

In the analysis of survival data, when nonproportional hazards are encountered, the Cox model is often extended to allow for a time-dependent effect by accommodating a varying coefficient. This extension, however, cannot resolve the nonproportionality caused by heterogeneity. In contrast, the heteroscedastic hazards regression (HHR) model is capable of modeling heterogeneity and thus can be applied when dealing with nonproportional hazards. In this paper, we study the application of the HHR model possibly equipped with varying coefficients. An LRR (logarithm of relative risk) plot is suggested when investigating the need to impose varying coefficients. Constancy and degeneration in the plot are used as diagnostic criteria. For the HHR model, a ‘piecewise effect’ (PE) analysis and an ‘average effect’ (AE) analysis are introduced. For the PE setting, we propose a score-type test for covariate-specific varying coefficients. The Stanford Heart Transplant data are analyzed for illustration. In the case of degeneration being destroyed by a polynomial covariate, piecewise constancy and/or monotonicity of the LRRs is considered as an alternative criterion based on the PE analysis. Finally, under the framework of the varying-coefficient HHR model, the meanings of the PE and AE analyses, along with their dynamic interpretation, are discussed.     

Adaptive power priors with empirical Bayes for clinical trials

Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much discussion as a way to increase the feasibility of trials in situations where patients are difficult to recruit. The best method to include this data is not yet clear, especially in the case when few historical studies are available. This paper looks at the power prior technique afresh in a binomial setting and examines some previously unexamined properties, such as Box P values, bias, and coverage. Additionally, it proposes an empirical Bayes‐type approach to estimating the prior weight parameter by marginal likelihood. This estimate has advantages over previously criticised methods in that it varies commensurably with differences in the historical and current data and can choose weights near 1 when the data are similar enough. Fully Bayesian approaches are also considered. An analysis of the operating characteristics shows that the adaptive methods work well and that the various approaches have different strengths and weaknesses.     

清教文化的性别形而上学： 对萨勒姆审巫案的诠释性解释

本文参照行动的文化结构化理论来解释北美历史上最大规模的猎巫事件--萨勒姆审巫案。对该案如何以及何以发生的理解与解释,需要考虑当时活跃在清教徒生活中复杂且被深刻感知的意义。本文揭示了清教文化中的危机。这场造成19个英国男女被绞死的审巫案是在原告、牧师及法官的主导下进行的,他们深植于一个充满意义的世界观中,操纵这一世界观,并最终借助合法暴力捍卫这一世界观。这个世界观是根据如下的紧张与矛盾节点而得以重建的：妇女在社会中的地位、殖民地与上帝之间的关系、世界如何运转的形而上信仰（如巫术与驱魂）。研究萨勒姆审巫案牵涉文化、诠释以及社会学解释的问题。为了回答这些问题,本文指出:（1）文化社会学的解释包含且不限于叙事性意向、意识、言说的意义及行动者的策略;（2）文化社会学的解释应把符号结构与政治经济学的互相嵌入纳入考虑范畴,但又注意不将前者化约为后者;（3）一个危机或事件的走势与参与其中的人们对危机的诠释之间相互纠缠。这意味着,文化社会学能够给出对社会生活的一种因果性诠释,从而超越在理解与解释之间长久存在的区分。     

Infant mortality by cause: A comparison of underlying and multiple cause designations

Through the use of unique data for the 1980–1982 birth cohorts in the State of Florida, USA, the present analysis examines the extent of variability in the social, economic, and demographic characteristics of decedents when grouped by detailed cause of infant death, across four models for identifying cause of death. The analysis first compares cause-of-death-specific infant, neonatal, and postneonatal mortality between each of the four cause-of-death models. Next, interest shifts to an examination of the variability among decedents, specific to cause of death, in a range of background, proximate, and immediate determinants of infant health and survival. Variability is evident in cause-specific mortality rates as well as in decedent characteristics across the cause-of-death models. These findings suggest that more attention be given to the mode of identifying cause of death in studies of infant mortality.     

Validation of A Heteroscedastic Hazards Regression Model

A Cox-type regression model accommodating heteroscedasticity, with a power factor of the baseline cumulative hazard, is investigated for analyzing data with crossing hazards behavior. Since the approach of partial likelihood cannot eliminate the baseline hazard, an overidentified estimating equation (OEE) approach is introduced in the estimation procedure. Its by-product, a model checking statistic, is presented to test for the overall adequacy of the heteroscedastic model. Further, under the heteroscedastic model setting, we propose two statistics to test the proportional hazards assumption. Implementation of this model is illustrated in a data analysis of a cancer clinical trial.     

Demographic research on infant mortality

I present an overview of the current status and recent trends in social-demographic research on infant mortality in the United States. Following from a fundamental interest in social differentials, recent research has come to emphasize the diverse linkages between social inequality and mortality. Research increasingly uses multilevel perspectives, combining background as well as proximate variables. Coupled with the availability of new individual-level data sets linking information from diverse sources, researchers are able to give increasing attention to the study of cause of death from interdisciplinary perspectives. The analysis of racial/ethnic infant mortality differentials is an example of these trends.This is a revised version of a paper presented in the session, U.S. Infant Mortality from Different Perspectives, of the 1987 annual meetings of the American Association for the Advancement of Science, February 11–15, Boston, MA.