An Exploratory Analysis of Societal Preferences for Research-Driven Quality of Life Improvements in Canada

Research in the humanities, arts, and social sciences (HASS) tends to have impacts that enhance quality of life (QOL) but that are not amenable to pricing in established markets. If the economic value of ‘non-market’ research impacts is ignored when making the business case for HASS research, society will under-invest in it. My goal in this research was to quantify priorities and tradeoffs Canadians were willing to make between broad QOL attributes. A national sample (N = 1,612; 14,571 observations) cleaved into five distinct latent classes, each of which varied according to their preferences and willingness to pay for QOL impacts. One class, comprising 21% of the sample, placed a strong priority on QOL indicators relating to people, community, and culture, and was willing to pay, on average, 892 per household per year for improvements across a full range of QOL attributes. While willing to pay892 per household per year for improvements across a full range of QOL attributes. While willing to pay 1,393 per household per year in total, a second segment, comprising 20% of the sample, focused heavily on benefits more likely to be derived from science and technology research. Willingness to pay for specific and aggregate improvements amongst the balance of the sample was lower. Diverse preferences regarding potential QOL impacts suggest that researchers will need to consider both the source and magnitude of public benefits arising from HASS research in the future.     

A location–allocation model for service providers with application to not-for-profit health care organizations

In this paper, we develop and solve a model for the location and allocation of specialized health care services such as traumatic brain injury (TBI) treatment. The model is based on and applied to one of the Department of Veterans Affairs’ integrated service networks. A cost minimization model with service proportion requirements is solved using simulated annealing. Large instances of the model with 100 candidate medical center locations and 15 open treatment units are solved in about 1000 s. In order to test the real-world applicability of our model, an extensive managerial experiment is conducted using data derived from our health care setting. In this experiment, the effects of three critical factors: (1) degree of centralization of services, (2) the role of patient retention as a function of distance to a treatment unit, and (3) the geographic density of the patient population are investigated with respect to the important trade-off between the cost of providing service and the need to provide such service. Our analysis shows that all three factors of the experiment are both relevant and useful to decision-makers when selecting locations for their services.     

Reviews and abstracts

This study examines leading explanations for unsafe sex in light of in‐depth interviews with 102 high‐risk gay and bisexual men in Toronto to see how well they engage with the social circumstances and reasoning processes of men in their sexual relationships. We argue that there is an inadequate fit between some of the leading explanations and the discursive accounts provided by high risk men themselves. Their accounts focus on unsafe sex occurring as a resolution to condom and erectile difficulties, through momentary lapses and trade offs, out of personal turmoil and depression, and as a byproduct of strategies of disclosure and intuiting safety. This study examines, in particular, the circumstances and rationales associated with men who identify their practices as “barebacking.” We conclude with recommendations for communicating prevention messages to those most at risk based on the self‐understandings of gay and bisexual men who most frequently practice unprotected sex.     

Evaluation of asymptotic approximations for a two-stage Bernoulli bandit

We consider a two-stage procedure for allocating two treatments to yield a total of N dichoto-mous responses, where one of the treatments has a known probability of success. In the first stage, observations may be made on either of the treatments and observed successes are discounted by a factor β. One of the treatments must be chosen for the second stage, where observed successes are no longer discounted. We adopt a Bayesian approach and develop a continuous time approximation for this problem that turns out to be identical to one developed in Petkau (J. Amer. Statist. Assoc. 73 (1978) 328). Examination of both stopping boundaries and Bayes risks demonstrates that suboptimal strategies provided by the solution of the continuous time problem are excellent approximations to the optimal strategies for the discrete time problem. A “continuity correction” developed by Cheroff and Petkau (Ann. Probab. 4 (1976) 875) plays an important role in enhancing the naive approximation provided by the solution of the continuous time problem.     

Robust weighted likelihood estimators with an application to bivariate extreme value problems

The authors achieve robust estimation of parametric models through the use of weighted maximum likelihood techniques. A new estimator is proposed and its good properties illustrated through examples. Ease of implementation is an attractive property of the new estimator. The new estimator downweights with respect to the model and can be used for complicated likelihoods such as those involved in bivariate extreme value problems. New weight functions, tailored for these problems, are constructed. The increased insight provided by our robust fits to these bivariate extreme value models is exhibited through the analysis of sea levels at two East Coast sites in the United Kingdom.     

A day school approach to aggressive adolescents.

A day school for aggressive adolescents, organized cooperatively by a school system, a youth guidance center, and a state hospital, is providing an effective alternative to institutionalization. Education and therapy are combined in a supportive setting.     

A new test for the presence of a normal mixture distribution based on the posterior Bayes factor

We present a new test for the presence of a normal mixture distribution, based on the posterior Bayes factor of Aitkin (1991). The new test has slightly lower power than the likelihood ratio test. It does not require the computation of the MLEs of the parameters or a search for multiple maxima, but requires computations based on classification likelihood assignments of observations to mixture components.