Beyond speculative robot ethics: a vision assessment study on the future of the robotic caretaker

In this article we develop a dialogue model for robot technology experts and designated users to discuss visions on the future of robotics in long-term care. Our vision assessment study aims for more distinguished and more informed visions on future robots. Surprisingly, our experiment also led to some promising co-designed robot concepts in which jointly articulated moral guidelines are embedded. With our model, we think to have designed an interesting response on a recent call for a less speculative ethics of technology by encouraging discussions about the quality of positive and negative visions on the future of robotics.     

Understanding neighbourhoods, communities and environments: new approaches for social work research

This article discusses some new ways in which social work research can explore the interaction between neighbourhoods and child and adult wellbeing. The authors note that social work practices are often criticised for taking an individualistic approach and paying too little attention to the service user's environment. The article uses examples of research projects from Chile, the United States of America and Wales, to discuss the use of spatially oriented research methods for understanding neighbourhood factors. Quantitative, qualitative and mixed methods approaches that are particularly appropriate for investigating social work relevant topics are discussed in turn, including quantitative and qualitative uses for geographical information systems (GIS), hierarchical linear modelling (HLM) for analysing spatially clustered data and qualitative mobile interviews. The article continues with a discussion of the strengths and limitations of using spatially orientated research designs in social work research settings and concludes optimistically with suggestions for future directions in this area.     

Union Instability as an Engine of Fertility? A Microsimulation Model for France

Opportunities for conceiving and bearing children are fewer when unions are not formed or are dissolved during the childbearing years. At the same time, union instability produces a pool of persons who may enter new partnerships and have additional children in stepfamilies. The balance between these two opposing forces and their implications for fertility may depend on the timing of union formation and parenthood. In this article, we estimate models of childbearing, union formation, and union dissolution for female respondents to the 1999 French Etude de l’Histoire Familiale. Model parameters are applied in microsimulations of completed family size. We find that a population of women whose first unions dissolve during the childbearing years will end up with smaller families, on average, than a population in which all unions remain intact. Because new partnerships encourage higher parity progressions, repartnering minimizes the fertility gap between populations with and those without union dissolution. Differences between the two populations are much smaller when family formation is postponed—that is, when union formation and dissolution or first birth occurs after age 30, or when couples delay childbearing after union formation.     

The Quality of Male Fertility Data in Major U.S. Surveys

Researchers continue to question fathers’ willingness to report their biological children in surveys and the ability of surveys to adequately represent fathers. To address these concerns, this study evaluates the quality of men’s fertility data in the 1979 and 1997 cohorts of the National Longitudinal Survey of Youth (NLSY79 and NLSY97) and in the 2002 National Survey of Family Growth (NSFG). Comparing fertility rates in each survey with population rates based on data from Vital Statistics and the U.S. Census Bureau, we document how the incomplete reporting of births in different surveys varies according to men’s characteristics, including their age, race, marital status, and birth cohort. In addition, we use Monte Carlo simulations based on the NSFG data to demonstrate how birth underreporting biases associations between early parenthood and its antecedents. We find that in the NSFG, roughly four out of five early births were reported; but in the NLSY79 and NLSY97, almost nine-tenths of early births were reported. In all three surveys, incomplete reporting was especially pronounced for nonmarital births. Our results suggest that the quality of male fertility data is strongly linked to survey design and that it has implications for models of early male fertility.     

Sense of Place and Health in Hamilton, Ontario: A Case Study

The concept of sense of place has received considerable attention by social scientists in recent years. Research has indicated that a person's sense of place is influenced by a number of factors including the built environment, socio-economic status (SES), well-being and health. Relatively few studies have examined sense of place at the neighbourhood level, particularly among communities exhibiting different levels of SES. This article investigates sense of place among three neighbourhood groups in Hamilton, Ontario representing areas of low, mixed and high SES. It analyses data from a 16-point sense of place scale derived from the Hamilton Household Quality of Life Survey carried out in 2010-2011 among 1,002 respondents. The paper found that sense of place was highest among residents of the high SES neighbourhood group as well as among home owners, people residing in single-detached homes, retired residents and those living in their neighbourhood for more than 10?years. From a health perspective, the paper found that a strong association existed between sense of place and self-perceived mental health across the three neighbourhood groups. Furthermore, by way of regression modeling, the paper examined the factors influencing health-related sense of place. Among the sample of respondents, a strong connection was found between housing, particularly home ownership, and high levels of health-related sense of place.     

A tilting algorithm for the estimation of fractional age survival probabilities

Life tables used in life insurance determine the age of death distribution only at integer ages. Therefore, actuaries make fractional age assumptions to interpolate between integer age values when they have to value payments that are not restricted to integer ages. Traditional fractional age assumptions as well as the fractional independence assumption are easy to apply but result in a non-intuitive overall shape of the force of mortality. Other approaches proposed either require expensive optimization procedures or produce many discontinuities. We suggest a new, computationally inexpensive algorithm to select the parameters within the LFM-family introduced by Jones and Mereu (Insur Math Econ 27:261–276, 2000). In contrast to previously suggested methods, our algorithm enforces a monotone force of mortality between integer ages if the mortality rates are monotone and keeps the number of discontinuities small.     

Investigating the assumption of homogeneity of treatment effects in clinical studies with application to meta-analysis

An important part of the evaluation of a therapy is an investigation of the assumption of homogeneity of its effect across pre-defined subpopulations. In this paper we describe simple graphical presentations that could be used to assess the homogeneity of treatment effect and identify outliers. The emphasis in the paper is on meta-analysis but the methods described can be generalized to other investigations.     

Improved population-based probability of developing cancer when direct estimates of the cancer-free population are available

Age-conditional probabilities of developing a first cancer represent the transition from being cancer-free to developing a first cancer. Natural inputs into their calculation are rates of first cancer per person-years alive and cancer-free. However these rates are not readily available because they require information on the cancer-free population. Instead rates of first cancer per person-years alive, calculated using as denominator the mid-year populations, available from census data, can be easily calculated from cancer registry data. Methods have been developed to estimate age-conditional probabilities of developing cancer based on these easily available rates per person-years alive that do not directly account for the cancer-free population. In the last few years models (Merrill et al., Int J Epidemiol 29(2):197-207, 2000; Mariotto et al., SEER Cancer Statistics Review, 2002; Clegg et al., Biometrics 58(3):684-688, 2002; Gigli et al., Stat Methods Med Res 15(3):235-253, 2006, and software (ComPrev:Complete Prevalence Software, Version 1.0, 2005) have been developed that allow estimation of cancer prevalence (DevCan: Probability of Developing or Dying of Cancer Software, Version 6.0, 2005). Estimates of population-based cancer prevalence allows for the estimation of the cancer-free population and consequently of rates per person-years alive and cancer-free. In this paper we present a method that directly estimates the age-conditional probabilities of developing a first cancer using rates per person-years alive and cancer-free obtained from prevalence estimates. We explore conditions when the previous and the new estimators give similar or different values using real data from the Surveillance, Epidemiology and End Results (SEER) program.     

Predicting treatment efficacy via quantitative magnetic resonance imaging: a Bayesian joint model

The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. Gibbs sampling and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model.     

Two-Sample Statistics for Testing the Equality of Survival Functions Against Improper Semi-parametric Accelerated Failure Time Alternatives: An Application to the Analysis of a Breast Cancer Clinical Trial

This paper presents two-sample statistics suited for testing equality of survival functions against improper semi-parametric accelerated failure time alternatives. These tests are designed for comparing either the short- or the long-term effect of a prognostic factor, or both. These statistics are obtained as partial likelihood score statistics from a time-dependent Cox model. As a consequence, the proposed tests can be very easily implemented using widely available software. A breast cancer clinical trial is presented as an example to demonstrate the utility of the proposed tests.