The SESS multisite collaborative research initiative: establishing common ground

Multidisciplinary intervention approaches are needed for meeting service needs for families in which substance abuse and mental health disorders may be interfering with child‐rearing. Experiences from the Starting Early Starting Smart (SESS) initiative, a 12‐site national collaborative investigation of integrating behavioural health services in early childhood and primary health care service settings for children aged 0–5 years and their families and caregivers, are described. This 4‐year applied research initiative was co‐funded by the Substance Abuse and Mental Health Services Administration of the US Department of Health and Human Services and Casey Family Programs, a private operating foundation. SESS, which was developed and implemented in 12 geographically and culturally diverse cities in the USA during 1997–2001, encouraged federal, state, and local public/private partnerships. Opportunities and challenges in using an inclusive, consensus‐based, stakeholder model to maximize study relevance and utility for researchers, practitioners, and fiscal sponsors are discussed, and lessons for multidisciplinary, multisite research collaborations are identified.     

Constitutions and Public Support for Welfare Policies*

Objectives. This research explores how constitutional designs affect a cross‐national gap in public support for welfare policies. We contend that the constitution's statements regarding the citizens' right to receive welfare services constrain elite discourse on social welfare, which in turn exerts a strong influence on the level of mass support for and ambivalence over welfare policies. Methods. Survey data from 15 consolidated democracies merged with country‐level data are analyzed using a hierarchical linear model. Results. Empirical analysis shows that citizens residing in countries with a more liberal constitution show more supportive and less ambivalent attitudes toward welfare policies. Conclusions. Our findings suggest that the political principles reflected in national constitutions explain the cross‐national gap in mass support for welfare policies.     

Bootstrap estimation and model selection for multivariate normal mixtures using parallel computing with graphics processing units

In applications of multivariate finite mixture models, estimating the number of unknown components is often difficult. We propose a bootstrap information criterion, whereby we calculate the expected log-likelihood at maximum a posteriori estimates for model selection. Accurate estimation using the bootstrap requires a large number of bootstrap replicates. We accelerate this computation by employing parallel processing with graphics processing units (GPUs) on the Compute Unified Device Architecture (CUDA) platform. We conducted a runtime comparison of CUDA algorithms between implementation on the GPU and that on a CPU. The results showed significant performance gains in the proposed CUDA algorithms over multithread CPUs.     

A comparison of midwife-led care versus obstetrician-led care for low-risk women in Japan

BackgroundContinuity of midwife-led care is recommended in maternity care because of its various positive outcomes. In Japan, midwife-led care is receiving broad attention as well. In order to popularise midwifery care within the entire system of perinatal care in Japan, there is a need to show evidence that continuity of midwife care for women will bring about positive outcomes.AimThe objectives of this study were to compare the health outcomes of women and infants who received midwife-led care with obstetrician-led care in Japan.MethodsThis was an observational study using non-random purposive sampling with a survey questionnaire. Settings where midwife-led care and obstetrician-led care were chosen by purposive samples. Participants were low-risk women who received antenatal care and delivered a term-singleton-infant at the participating settings during the research period. Measurements were: Women-centred care pregnancy questionnaire, Stein's maternity blues questionnaire, and Edinburgh Postnatal Depression Scale.FindingsMidwife-led care was perceived by women to be beneficial and had no adverse outcomes compared to obstetrician-led care. Main findings are: (1) Perception of Women-centred care was higher; (2) Less premature rupture of membranes, and the Apgar scores of the infants were similar; (3) Exclusively breast-feeding during hospitalisation and at one-month postpartum; (4) Stein's maternity blues scale scores was lower in women who received midwife-led care than those who received obstetrician-led care.ConclusionsContinuity of midwife-led care was perceived by women to be beneficial and had no adverse outcomes. Therefore, midwife-led care in low-risk pregnancy could be applicable and recommended.     

A variable selection method for detecting abnormality based on the T2 test

This paper proposes a variable selection method for detecting abnormal items based on the T² test when the observations on abnormal items are available. Based on the unbiased estimates of the powers for all subsets of variables, the variable selection method selects the subset of variables that maximizes the power estimate. Since more than one subsets of variables maximize the power estimate frequently, the averaged p-value of the rejected items is used as a second criterion. Although the performance of the method depends on the sample size for the abnormal items and the true power values for all subsets of variables, numerical experiments show the effectiveness of the proposed method. Since normal and abnormal items are simulated using one-factor and two-factor models, basic properties of the power functions for the models are investigated.     

A Note on the Max-Min 0-1 Knapsack Problem

In this paper we propose new lower and upper bounds for the max-min 0-1 knapsack problem, employing a mixture of two relaxations. In addition, in order to expose whether the bounds are practical or not, we implement a method incorporating the bounds to achieve an optimal solution of the problem.