Contractual audit and mental health rehabilitation: a study of formulating effectiveness in a Finnish supported housing unit

Saario S, Raitakari S. Contractual audit and mental health rehabilitation: a study of formulating effectiveness in a Finnish supported housing unit Int J Soc Welfare 2010: 19: 321–329 © 2010 The Author(s), Journal compilation © 2010 Blackwell Publishing Ltd and International Journal of Social Welfare. Mental health NGOs in Western Europe are increasingly managed by contractual audit procedures. This article concerns how contractual audit and its emphasis on effectiveness of care impact on the practices of long‐term mental health rehabilitation. To demonstrate this, a case study of a Finnish NGO that provides supported housing is presented. The study looks at how service purchasing practices, as stated in the contract between the municipality and the NGO, are reflected in the meetings among practitioners. Documentary and meeting data were utilised together with Mitchell Dean's notion of technologies of agency. It was found that practitioners actively sought to show the effectiveness of their everyday work in terms of contractual audit by demonstrating both the economic and progressive aspects of care. Thus, professional competency in mental health rehabilitation appears to entail both the skills of care interventions and the ability to perform these interventions as efficient and financially accountable activities.     

金融危机下中国创业投资“囚徒困境”演化研究

创业投资家和创业企业家的＂囚徒困境＂是制约中国创业投资进一步发展的主要障碍之一。对短期利益的追求和缺乏信任是造成中国创业投资＂囚徒困境＂的两个重要原因。在金融危机背景下,＂囚徒困境＂的演化将朝着有利于创业投资家的方向发展。金融危机虽然能改变博弈的结果,却未能改变＂囚徒困境＂的成因。     

Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.     

New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

Wavelet goodness-of-fit test for dependent data

We introduce a new goodness-of-fit test which can be applied to hypothesis testing about the marginal distribution of dependent data. We derive a new test for the equivalent hypothesis in the space of wavelet coefficients. Such properties of the wavelet transform as orthogonality, localisation and sparsity make the hypothesis testing in wavelet domain easier than in the domain of distribution functions. We propose to test the null hypothesis separately at each wavelet decomposition level to overcome the problem of bi-dimensionality of wavelet indices and to be able to find the frequency where the empirical distribution function differs from the null in case the null hypothesis is rejected. We suggest a test statistic and state its asymptotic distribution under the null and under some of the alternative hypotheses.     

‘Trapped between two worlds’ – Muslim Palestinian women from Israel in Jordanian universities: new identity and the price it demands

Interviews were held with 12 Muslim Palestinian women from Israel, presently studying in Jordan (6) or who had completed their higher education in Jordan (6). They explained the factors that pushed or pulled them to study in Jordan, the independence that they experienced there, the empowerment they achieved, and the price they paid when they returned to Arab society in Israel. The Arab cultural space in Jordan is defined as both foreign and close, due to its geographical and cultural proximity, and yet its distance from home and patriarchal supervision. The research findings indicate that this situation influences the formation of these women's gender identity and their empowerment but also creates much pain and conflict. The Palestinian women's new identity, formed during their studies, assists them in their efforts to reintegrate and establish their status when they return from their academic studies abroad to their society of origin, Muslim Arab society in Israel.     

The EPA Health Risk Assessment of Methylcyclopentadienyl Manganese Tricarbonyl (MMT)

This paper describes the U.S. Environmental Protection Agency's assessment of potential health risks associated with the possible widespread use of a manganese (Mn)-based fuel additive, methylcyclopentadienyl manganese tricarbonyl (MMT). This assessment was significant in several respects and may be instructive in identifying certain methodological issues of general relevance to risk assessment. A major feature of the inhalation health risk assessment was the derivation of Mn inhalation reference concentration (RfC) estimates using various statistical approaches, including benchmark dose and Bayesian analyses. The exposure assessment component used data from the Particle Total Exposure Assessment Methodology (PTEAM) study and other sources to estimate personal exposure levels of particulate Mn attributable to the permitted use of MMT in leaded gasoline in Riverside, CA, at the time of the PTEAM study; on this basis it was then possible to predict a distribution of possible future exposure levels associated with the use of MMT in all unleaded gasoline. Qualitative as well as quantitative aspects of the risk characterization are summarized, along with inherent uncertainties due to data limitations.     

INSIGHTS ON SERVICE SYSTEM DESIGN FROM A NORMAL APPROXIMATION TO ERLANG'S DELAY FORMULA

We show how a simple normal approximation to Erlang's delay formula can be used to analyze capacity and staffing problems in service systems that can be modeled as M/M/s queues. The numbers of servers, s, needed in an M/M/s queueing system to assure a probability of delay of, at most, p can be well approximated by s ≅ p + z_***I-p+, where z_1-p, is the (1 - p)th percentile of the standard normal distribution and ρ, the presented load on the system, is the ratio of Λ, the customer arrival rate, to μ, the service rate. We examine the accuracy of this approximation over a set of parameters typical of service operations ranging from police patrol, through telemarketing to automatic teller machines, and we demonstrate that it tends to slightly underestimate the number of servers actually needed to hit the delay probability target—adding one server to the number suggested by the above formula typically gives the exact result. More importantly, the structure of the approximation promotes operational insight by explicitly linking the number of servers with server utilization and the customer service level. Using a scenario based on an actual teleservicing operation, we show how operations managers and designers can quickly obtain insights about the trade-offs between system size, system utilization and customer service. We argue that this little used approach deserves a prominent role in the operations analyst's and operations manager's toolbags.     

Three-part model for fractional response variables with application to Chinese household health insurance coverage

A survey on health insurance was conducted in July and August of 2011 in three major cities in China. In this study, we analyze the household coverage rate, which is an important index of the quality of health insurance. The coverage rate is restricted to the unit interval [0, 1], and it may differ from other rate data in that the “two corners” are nonzero. That is, there are nonzero probabilities of zero and full coverage. Such data may also be encountered in economics, finance, medicine, and many other areas. The existing approaches may not be able to properly accommodate such data. In this study, we develop a three-part model that properly describes fractional response variables with non-ignorable zeros and ones. We investigate estimation and inference under two proportional constraints on the regression parameters. Such constraints may lead to more lucid interpretations and fewer unknown parameters and hence more accurate estimation. A simulation study is conducted to compare the performance of constrained and unconstrained models and show that estimation under constraint can be more efficient. The analysis of household health insurance coverage data suggests that household size, income, expense, and presence of chronic disease are associated with insurance coverage.