Consultation,for a Change? Engaging Users and Communities in the Policy Process

The process of consultation has become integral to the development, implementation and evaluation of a raft of UK health and social policies. However, the current bewildering patchwork of area–based initiatives means that, in many localities, it is impossible to evaluate the outcomes of particular targeted initiatives, let alone make sense of local planning consultations, Best Value reviews and (multi–agency) service reviews which run concurrently. The cumulative effects of this consultation "overload" threaten to swamp both local authorities and their service users. Consul–tation is itself a crucial yet deeply problematic process. There is an official view which holds that an "old" model of consultation—often tokenistic and unrepresentative—is being replaced with a "new" one. This paper examines and challenges that view in relation to the key policy areas of housing, social services and policing. It also pays particular attention to, and problematizes, the notion of "hard–to–reach groups", which is so dominant in the discourse of consultation. The paper argues that developing appropriate tools and recognizing that consultation is a process—not an event—are essential starting points in addressing these problems. The next step is to reconcile the principles of both evidence–based policy and user–led services into a strategic (and "joined–up") framework. But, when all this is accomplished, we still need to question the political and fiscal contexts in which policy–making takes place and within which the process of consultation is itself bounded.     

Model-free variable selection

Summary.  The importance of variable selection in regression has grown in recent years as computing power has encouraged the modelling of data sets of ever-increasing size. Data mining applications in finance, marketing and bioinformatics are obvious examples. A limitation of nearly all existing variable selection methods is the need to specify the correct model before selection. When the number of predictors is large, model formulation and validation can be difficult or even infeasible. On the basis of the theory of sufficient dimension reduction, we propose a new class of model-free variable selection approaches. The methods proposed assume no model of any form, require no nonparametric smoothing and allow for general predictor effects. The efficacy of the methods proposed is demonstrated via simulation, and an empirical example is given.     

The Collection and Management of Occupational Exposure Data

This presentation addresses a number of issues pertinent to the collection and management of occupational exposure data and offers suggestions to those persons who are developing systems to handle large volumes of occupational exposure data. A perspective is taken that aims to meet the traditional objectives of industrial hygienists while accommodating epidemiologic needs for linking occupational exposure and health outcome data. The suggestions are based on experience gained through the retrospective use of industrial hygiene data in a large number of epidemiologic studies.     

A bayesian wls approach to generalized linear models

We use a Bayesian approach to fitting a linear regression model to transformations of the natural parameter for the exponential class of distributions. The usual Bayesian approach is to assume that a linear model exactly describes the relationship among the natural parameters. We assume only that a linear model is approximately in force. We approximate the theta-links by using a linear model obtained by minimizing the posterior expectation of a loss function.While some posterior results can be obtained analytically considerable generality follows from an exact Monte Carlo method for obtaining random samples of parameter values or functions of parameter values from their respective posterior distributions. The approach that is presented is justified for small samples, requires only one-dimensional numerical integrations, and allows for the use of regression matrices with less than full column rank. Two numerical examples are provided.     

Understanding Food Insecurity Among Latino Immigrant Families in Rural America

Using ecological theory, this study investigated how low-income rural Latino immigrant families succeeded or failed to meet their food needs over time. Interviews with ten families purposively selected to represent consistently food secure, fragile, and consistently food insecure groups were intensively analyzed using a case study approach. Achieving and maintaining food security was a complicated task and a constant struggle for families. Success or failure was influenced by factors at multiple ecological levels including family characteristics, access to social networks and community support, and the local economy. These findings can inform practitioners and policymakers seeking to identify strategies and policies that will support Latino immigrant families as they work toward improving their well-being.     

Gender Role Models in Fictional Novels for Emerging Adult Lesbians

Novels provide role models for young adult lesbians and thus may influence their identity development. This study focused on 16 lesbian protagonists identified in 11 young adult novels that received 2011 Lambda Literary Award nominations. Content analyses revealed six themes. Three themes defied traditional gender stereotypes: Asserting Oneself, Pursuing Intimacy with Another Woman, and Breaking Free of Constraints to Authentic Self-Expression. Three themes reinforced gender stereotypes: Negative Emotional Experiences Associated with Lesbian Identity, Traditional Masculine Gender Expression, and Traditional Gender Role-Based Sexual Scripts. Each theme is discussed in light of its possible contribution to lesbian identity development.