Distribution of the random future life expectancies in log-bilinear mortality projection models

Projected life tables are obtained from forecasting methods and account for future improvements in longevity. Since the future path of mortality is unknown, working with projected life tables makes the survival probabilities stochastic. The resulting demographic indicators in turn become random variables. This paper aims to study the distribution of period and cohort life expectancies derived from projected life tables. To fix the ideas, we adopt here the standard Lee–Carter framework, where the future forces of mortality are decomposed in a log-bilinear way. Exact formulas are derived for period life expectancies, and approximations are proposed for cohort life expectancies. In the latter case, numerical illustrations based on Belgian population data show that the relative accuracy is remarkable.     

Homelessness in Europe and the United States: A Comparison of Prevalence and Public Opinion

Random samples of 250–435 adults were interviewed by telephone in five different nations ( N = 1,546): Belgium, Germany, Italy, the UK, and the United States. The interview included questions on respondent attitudes, knowledge, and opinions regarding homelessness; respondents' own personal experiences with homelessness and homeless people; and demographic characteristics of the respondents. The highest rates for lifetime literal homelessness were found in the UK (7.7%) and United States (6.2%), with the lowest rate in Germany (2.4%), and intermediate rates in Italy (4.0%) and Belgium (3.4%). Less compassionate attitudes toward the homeless were also found on many dimensions in the United States and the UK. Possible explanations of these findings, drawn from various theoretical perspectives, and policy implications are provided .     

A fully nonparametric approach in survival models with explanatory variables

The most widely used model for multidimensional survival analysis is the Cox model. This model is semi-parametric, since its hazard function is the product of an unspecified baseline hazard, and a parametric functional form relating the hazard and the covariates. We consider a more flexible and fully nonparametric proportional hazards model, where the functional form of the covariates effect is left unspecified. In this model, estimation is based on the maximum likelihood method. Results obtained from a Monte-Carlo experiment and from real data are presented. Finally, the advantages and the limitations of the approacha are discussed.     

The Instruction set of Questionnaires can Affect the Structure of the Data: Application to Self-Rated State Anxiety

The present study tested the assumption that self-ratings, such as those used for measuring state anxiety, do not measure a one-dimensional transcendent entity but involve decisions based on a multi-dimensional judgment. Two groups of subjects were presented with a balanced nine-item state anxiety questionnaire. Each group received a different set of instructions (a standard set and an altered instruction set suggesting unidimensionality of the questions in the questionnaire). It was hypothesized that this change in instructions would impact the structure of the data. The impact of the instructions was detectable at the level of the observed correlation between the negative and positive composites, Cohen's (1988) q=–0.27$. A multigroup confirmatory factor analysis showed that positive and negative wording factors correlated more strongly when unidimensionality was suggested than when standard instructions were used, q=–0.54. Theoretical and practical implications are discussed.     

Job-related resources,leader–member exchange and well-being – a longitudinal study

Studies show that high-quality reciprocal relationships between leaders and followers are positively associated with employee well-being. However, the pathways for promoting this health-enhancing leadership behaviour have not yet been well explored, and most studies have been cross-sectional. The primary objective of this study was to examine the effects of the interplay between job-related resources and leader–member exchange (LMX) on employee well-being, measured as the emotional exhaustion component of burnout. We applied a two-wave paper-and-pencil design, with an 11-month interval, in a sample of 343 employees working in the German healthcare sector. By means of structural equation modelling, we found that job-related resources (role clarity, meaningfulness and predictability) predicted higher quality LMX, which in turn related to lower levels of emotional exhaustion. The results support the important role of job resources in stimulating health-relevant aspects of leadership behaviour, and indicate ways in which leaders can promote employee health and well-being.     

Measuring the adoption of innovation. A typology of EU countries based on the Innovation Survey

Based on the Community Innovation Survey, this paper suggests new indicators of innovation adoption. The magnitude of innovation adoption is assessed for 22 EU countries and different industries. The most striking feature is the correlation between the innovation activities and the adoption rate. Countries with strong R&D and human resources and high innovation output exhibit the highest adoption rates. This supports the idea that innovation adoption requires an absorption capability. In addition, the specificities of each country regarding the prevailing types of innovation and adoption (product or process, cooperation-based adoption or internal adoption) allow us to draw up a typology of the EU countries, for which a specific geographical pattern is observed.     

“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.