Gay Abuse Screening Protocol (GASP):Screening for Abuse in Gay Male Relationships

ABSTRACT

Thirty-two male patients in gay relationships and eight family physicians were recruited from a family practice in order to determine comfort with an eight-question Gay Abuse Screening Protocol (GASP). The GASP was administered during a typical clinical encounter. After the encounter, physicians and patients each completed a 5-point Likert Scale questionnaire to assess their comfort levels with each of the 8 GASP questions (Likert Scale: 1?=?not at all comfortable to 5?=?very comfortable). The mean comfort score was high (Likert?>4) for both patients (4.16 ± 0.18) and physicians (4.71 ± 0.18). However, mean comfort scores were significantly lower for abused patients (3.26 ± 0.75) than nonabused patients (4.57 ± 0.26). Patients were comfortable (Likert?>3) with 76.2% of GASP items while physicians were comfortable with all GASP items.     

The social work services in screening process of compulsory treatment interventions in addicts in Iran: A content analysis study

Compulsory treatment has had a detrimental impact on drug abuse treatment policies and programs in Iran. Physicians are currently required to conduct initial treatment screening. Social workers are a part of the treatment team but have no authority to initiate a compulsory drug treatment plan. For this reason, the present article investigates social work service participation in the process of compulsory drug treatment. The study methodology is content analysis. Nine social workers participated in seven focus group discussions. Overall, 110 codes were extracted from the sessions and were categorized into five main themes: interview and initial assessment; referral; investigation of ineligibility criteria; report to judicial authority; and follow‐up and advocacy. The results of this research suggest amending executive bylaws based on a social work approach to the screening process for compulsory drug treatment.     

Proactive Screening for Health Needs in United Way's 2-1-1 Information and Referral Service

ABSTRACT

Cancer disproportionately affects the underserved. United Way 2-1-1 is an information and referral system that links underserved populations to community services. This study explores the feasibility of integrating proactive screening and referral to health services into 2-1-1. A cancer risk assessment was administered to callers (n = 297), measuring their need for 6 cancer control services. A subset of respondents was randomized to receive generic or tailored referrals to needed services. Nearly all participants (85%) needed at least one of the services. Those who received tailored referrals were more likely to make appointments. Future research will explore approaches to address and eliminate health disparities through 2-1-1.     

Cancer Screening Barriers for Community-based Older Hispanics and Caucasians

Despite advances in screening and early detection, ethnic minority populations, ages 65 and older, are less likely than Caucasians to participate in cancer screening services. Empirical research indicates that older ethnic minorities have cultural values that influence their behaviors. Addressing culturally relevant communication to better understand those values may increase participation in cancer screening. The study reported is a secondary analysis of qualitative data gathered from focus groups. Utilizing an interdisciplinary analytical lens, we compared older Hispanic and Caucasian's cultural values and their screening behaviors. Suggested psychosocial interventions are discussed to assist providers in their ongoing efforts to promote cancer screening.     

The Use of Reality Orientation in Adult Foster Care Homes:

The emphasis in recent years on providing long-term care in community-based alternatives to the institutional setting can help to facilitate the development of new and exciting opportunities for enrichment in the lives of individuals whose educational needs often go unmet. Reality orientation has been demonstrated to be an effective approach to the treatment and prevention of loss of memory, confusion, and disorientation among institutionalized adults. However, certain problems can arise that may limit the potential benefits of reality orientation programs in institutions. This paper explores the rationale for considering the use of reality orientation in one alternative living arrangement, the adult foster care home. The personalized, family-like environment of the adult foster care setting might help to minimize or even eliminate many of the problems that detract from reality orientation programs in larger institutions.     

Bayesian assessment of times to diagnosis in breast cancer screening

Breast cancer is one of the diseases with the most profound impact on health in developed countries and mammography is the most popular method for detecting breast cancer at a very early stage. This paper focuses on the waiting period from a positive mammogram until a confirmatory diagnosis is carried out in hospital. Generalized linear mixed models are used to perform the statistical analysis, always within the Bayesian reasoning. Markov chain Monte Carlo algorithms are applied for estimation by simulating the posterior distribution of the parameters and hyperparameters of the model through the free software WinBUGS.     

Investigation about a screening step in model selection

In many studies a large number of variables is measured and the identification of relevant variables influencing an outcome is an important task. For variable selection several procedures are available. However, focusing on one model only neglects that there usually exist other equally appropriate models. Bayesian or frequentist model averaging approaches have been proposed to improve the development of a predictor. With a larger number of variables (say more than ten variables) the resulting class of models can be very large. For Bayesian model averaging Occam’s window is a popular approach to reduce the model space. As this approach may not eliminate any variables, a variable screening step was proposed for a frequentist model averaging procedure. Based on the results of selected models in bootstrap samples, variables are eliminated before deriving a model averaging predictor. As a simple alternative screening procedure backward elimination can be used. Through two examples and by means of simulation we investigate some properties of the screening step. In the simulation study we consider situations with fifteen and 25 variables, respectively, of which seven have an influence on the outcome. With the screening step most of the uninfluential variables will be eliminated, but also some variables with a weak effect. Variable screening leads to more applicable models without eliminating models, which are more strongly supported by the data. Furthermore, we give recommendations for important parameters of the screening step.     

Fast Bayesian variable screenings for binary response regressions with small sample size

Screening procedures play an important role in data analysis, especially in high-throughput biological studies where the datasets consist of more covariates than independent subjects. In this article, a Bayesian screening procedure is introduced for the binary response models with logit and probit links. In contrast to many screening rules based on marginal information involving one or a few covariates, the proposed Bayesian procedure simultaneously models all covariates and uses closed-form screening statistics. Specifically, we use the posterior means of the regression coefficients as screening statistics; by imposing a generalized g-prior on the regression coefficients, we derive the analytical form of their posterior means and compute the screening statistics without Markov chain Monte Carlo implementation. We evaluate the utility of the proposed Bayesian screening method using simulations and real data analysis. When the sample size is small, the simulation results suggest improved performance with comparable computational cost.     

Sure explained variability and independence screening

In the era of Big Data, extracting the most important exploratory variables available in ultrahigh-dimensional data plays a key role in scientific researches. Existing researches have been mainly focusing on applying the extracted exploratory variables to describe the central tendency of their related response variables. For a response variable, its variability characteristic is as much important as the central tendency in statistical inference. This paper focuses on the variability and proposes a new model-free feature screening approach: sure explained variability and independence screening (SEVIS). The core of SEVIS is to take the advantage of recently proposed asymmetric and nonlinear generalised measures of correlation in the screening. Under some mild conditions, the paper shows that SEVIS not only possesses desired sure screening property and ranking consistency property, but also is a computational convenient variable selection method to deal with ultrahigh-dimensional data sets with more features than observations. The superior performance of SEVIS, compared with existing model-free methods, is illustrated in extensive simulations. A real example in ultrahigh-dimensional variable selection demonstrates that the variables selected by SEVIS better explain not only the response variables, but also the variables selected by other methods.     

On the performance of three-stage group screening experiments

This paper examines the performance of three-stage group screening in terms of the mean number of tests needed; and the proportion of active factors correctly detected by the screening plan, A linear cost function is also proposed, To evaluate performance, random grouping and a constant signa1-to -noise ratio for all active factors, are assumed.