Creating safe spaces: designing day shelters for people experiencing homelessness

Day shelter programs are an important resource for people experiencing homelessness, offering respite from inclement weather, social stigma, victimization, and access to essential services and resources. Creating functional day shelter spaces requires that staff, services, and facilities support the needs of this vulnerable and marginalized population. Little guidance, however, is available regarding the design of day shelter spaces that support service engagement and positive service outcomes. This systematic review was conducted to support the development of a new day shelter in Fort Worth, Texas, identifying and reviewing interdisciplinary resources relevant to the design of these facilities. Based on the review, a preliminary taxonomy emphasizing day shelter client safety, security, dignity and health and well-being is proposed. Additional discussion identifies practical considerations affecting the implementation of the taxonomy, design priorities, and how the taxonomy was applied to the design of the Fort Worth day shelter.     

Reputation and Mechanism Choice in Procurement Auctions: An Experiment

We experimentally study the role of reputation in procurement using two common mechanisms: price‐based and buyer‐determined auctions. While buyers are bound to buy from the lowest bidder in price‐based auctions, they can choose between bidders in buyer‐determined auctions. Only the latter buyers can consider the reputation of bidders. We find that bidders supply higher quality in buyer‐determined auctions leading to higher market efficiencies in these auctions. Accordingly, buyers prefer the buyer‐determined auction over the price‐based auction, while only half of the bidders do so. A more detailed analysis of buyers' and bidders' behavior and profits provides insights into their mechanism choice.     

The empowerment of people with disabilities through qualitative research

The purpose of this article is to explore the use of qualitative research in vocational rehabilitation as a means of empowering people with disabilities to (a) take greater control of their lives and (b) directly inform public policy and vocational rehabilitation service delivery. After exploring various definitions of the construct empowerment, we provide a rationale for the use of qualitative research as an empowerment mechanism based on (a) philosophies espoused by leaders of the independent living movement, (b) disability legislation, and (c) a multi-constituency call for more consumer involvement in research. We then describe empirical properties of various qualitative methodologies that lend themselves to consumer empowerment. The article concludes with several examples of qualitative approaches to scientific inquiry from the rehabilitation literature that include many of the empowerment aspects described herein.     

It takes a mother to practise breastfeeding: Women's perceptions of breastfeeding during the period of intention

BackgroundIn the Netherlands, 81% of mothers initiate breastfeeding. After one month the percentage of mothers still breastfeeding drops, despite positive intentions. Little is known about women's perceptions of breastfeeding during the period of intention.AimThis qualitative study aimed to gain insight into these perceptions among first-time mothers from middle and high socioeconomic backgrounds in the northern part of the Netherlands.MethodsWe used the theory of planned behaviour as the deductive model. In 2008, 16 in-depth interviews were conducted with 8 mothers who intended to breastfeed. The interviews were conducted at two time points (prepartum and postpartum) and covered the same period (that is, from the time when the intention was formed until after childbirth). The interviews were transcribed verbatim and analysed using grounded theory.FindingsFive inductive themes were identified: combining breastfeeding with work, learning about breastfeeding, making arrangements for childbirth, reflecting on the intention, and becoming a mother. During the extended period of intention, the women anticipated breastfeeding, but were cautious in expressing their intentions. They felt that the experience of becoming a mother would be critical to their breastfeeding outcomes.ConclusionThe theory of planned behaviour has been widely used in breastfeeding research. However, the period of intention is relatively long for breastfeeding. Rather than recommending an intensification of antenatal breastfeeding education, recommendations must incorporate the awareness that practising breastfeeding should not be considered the continuous outcome of the intention to do so – it takes a mother to practise breastfeeding.     

Improved nearest neighbor classifiers by weighting and selection of predictors

Nearest neighborhood classification is a flexible classification method that works under weak assumptions. The basic concept is to use the weighted or un-weighted sums over class indicators of observations in the neighborhood of the target value. Two modifications that improve the performance are considered here. Firstly, instead of using weights that are solely determined by the distances we estimate the weights by use of a logit model. By using a selection procedure like lasso or boosting the relevant nearest neighbors are automatically selected. Based on the concept of estimation and selection, in the second step, we extend the predictor space. We include nearest neighborhood counts, but also the original predictors themselves and nearest neighborhood counts that use distances in sub dimensions of the predictor space. The resulting classifiers combine the strength of nearest neighbor methods with parametric approaches and by use of sub dimensions are able to select the relevant features. Simulations and real data sets demonstrate that the method yields better misclassification rates than currently available nearest neighborhood methods and is a strong and flexible competitor in classification problems.     

On kernel machine learning for propensity score estimation under complex confounding structures

Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive interactions) embedded in these observational data often pose major analytical challenges for proper analysis to draw valid conclusions. Furthermore, often made available as electronic health records (EHRs), these data are usually massive with hundreds of thousands observational records, which introduce additional computational challenges. In this paper, for comparative effectiveness analysis, we propose a statistically robust yet computationally efficient propensity score (PS) approach to adjust for the complex confounding structures. Specifically, we propose a kernel-based machine learning method for flexibly and robustly PS modeling to obtain valid PS estimation from observational data with complex confounding structures. The estimated propensity score is then used in the second stage analysis to obtain the consistent average treatment effect estimate. An empirical variance estimator based on the bootstrap is adopted. A split-and-merge algorithm is further developed to reduce the computational workload of the proposed method for big data, and to obtain a valid variance estimator of the average treatment effect estimate as a by-product. As shown by extensive numerical studies and an application to postoperative pain EHR data comparative effectiveness analysis, the proposed approach consistently outperforms other competing methods, demonstrating its practical utility.     

Linear Models Methods for Some Rank Function Analyses of Ordinal Categorical Data

This paper is concerned with the analysis of ordinal data through linear models for rank function measures.Primary attention is directed at pairwise Mann-Whitney statistics for which dimension reduction is managed by use of a Bradley-Terry log-linear structure.The nature of linear models for such quantities is contrasted with that for mean ranks (or ridits).Aspects of application are illustrated with an example for which results of other methods are also given.