A Phenomenological Case Study of the Multicultural Counseling Experience of Students and Faculty in Relation to Their Perceptions of Their Multicultural Competency and CACREP Standards

The shifting population demographics of the United States are an unmistakable sign that the work of professional counselors and educators will continue to see an increase in the diversity of their client populations. It can be surmised, that paralleling this population change will be a subsequent increase in the demand for multicultural sensitive education and counseling, with particular attention being given to oppressed and marginalized groups who have been traditionally underserved. The counseling profession, guided by the American Counseling Association\u27s (ACA) Code of Ethics, has unequivocally stated that professional counselors need to be proficient at providing multicultural competent counseling services to the growing and diverse multicultural population in the United States. It is not only a job requirement but a professional responsibility for counselors to be properly trained and prepared to effectively work with diverse and complex segments of our society. This study looks at the link between CACREP standards, graduate multicultural counseling training, and the resulting impact on the perceived competence of counselor trainees. It is a phenomenological case study exploring the experiences and perceptions of students and faculty in a CACREP accredited counseling program\u27s multicultural course

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Posttraumatic Stress Disorder, Depression, and Prolonged Grief Disorder in families bereaved by a traumatic workplace death: the need for satisfactory information and support

The impact of traumatic workplace death on bereaved families, including their mental health and well-being, has rarely been systematically examined. This study aimed to document the rates and key correlates of probable posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and prolonged grief disorder (PGD) in family members following a workplace injury fatality. The hidden nature of the target population necessitated outreach recruitment techniques, including the use of social media, newspaper articles, radio interviews, and contact with major family support organizations. Data were collected using a cross-sectional design and international online survey. The PCL-C (PTSD), the PHQ-8 (MDD), and PG-13 (PGD) were used to measure mental health disorders. All are well-established self-report measures with strong psychometric qualities. Participants were from Australia (62%), Canada (17%), the USA (16%), and the UK (5%). The majority were females (89.9%), reflecting the gender distribution of traumatic workplace deaths (over 90% of fatalities are male). Most were partners/spouses (38.5%) or parents (35%) and over half (64%) were next of kin to the deceased worker. Most deaths occurred in the industries that regularly account for more than 70 percent of all industrial deaths—construction, manufacturing, transport, and agriculture forestry and fishing. At a mean of 6.40 years (SD = 5.78) post-death, 61 percent of participants had probable PTSD, 44 percent had probable MDD, and 43 percent had probable PGD. Logistic regressions indicated that a longer time since the death reduced the risk of having each disorder. Being next of kin and having a self-reported mental health history increased the risk of having MDD. Of the related information and support variables, having satisfactory support from family, support from a person to help navigate the post-death formalities, and satisfactory information about the death were associated with a decreased risk of probable PTSD, MDD, and PGD, respectively. The findings highlight the potential magnitude of the problem and the need for satisfactory information and support for bereaved families

Real Lives: findings from the All-Ireland Gay Men’s Sex Surveys, 2003 and 2004

Duration: March 2000 - September 2010 Sigma Research has been working with Ireland's Gay Health Network (GHN) since 2000. GHN is an umbrella organisation working towards gay men's health and HIV prevention. GHN instigated a community-based, self-completion survey to take place across The Republic of Ireland and Northern Ireland during the summer of 2000 and commissioned Sigma Research to work with them. This large-scale community research project was the third such survey among gay men in Ireland, and built on previous findings. After the development and piloting of the survey, recruitment commenced at Dublin Pride in June 2000 and continued throughout the summer at similar events in Belfast, Derry, Galway, Limerick and Waterford. Recruitment in bars and clubs took place in Dublin and Cork, and social groups in more rural area were sent copies of the questionnaire and a request to distribute them to their members. 1,290 questionnaires were returned by gay men (81%), bisexual men (11%) and other homosexually active men living in Ireland. 19% of all respondents lived in Northern Ireland. A full survey report, including implications for HIV prevention planning is available to download. Since 2003 Gay Health Network members - particularly The Gay Men's Health Service (Health Services Executive) and the Rainbow Project, Northern Ireland - have collaborated with our online UK version of the Gay Men’s Sex Survey (Vital Statistics) by promoting it to men in Ireland via community websites and postcards distributed on the gay scene

The Merging History of Massive Black Holes

We investigate a hierarchical structure formation scenario describing the evolution of a Super Massive Black Holes (SMBHs) population. The seeds of the local SMBHs are assumed to be 'pregalactic' black holes, remnants of the first POPIII stars. As these pregalactic holes become incorporated through a series of mergers into larger and larger halos, they sink to the center owing to dynamical friction, accrete a fraction of the gas in the merger remnant to become supermassive, form a binary system, and eventually coalesce. A simple model in which the damage done to a stellar cusps by decaying BH pairs is cumulative is able to reproduce the observed scaling relation between galaxy luminosity and core size. An accretion model connecting quasar activity with major mergers and the observed BH mass-velocity dispersion correlation reproduces remarkably well the observed luminosity function of optically-selected quasars in the redshift range 1<z<5. We finally asses the potential observability of the gravitational wave background generated by the cosmic evolution of SMBH binaries by the planned space-born interferometer LISA.Comment: 4 pages, 2 figures, Contribute to "Multiwavelength Cosmology", Mykonos, Greece, June 17-20, 200

A Non-Sequential Representation of Sequential Data for Churn Prediction

We investigate the length of event sequence giving best predictions when using a continuous HMM approach to churn prediction from sequential data. Motivated by observations that predictions based on only the few most recent events seem to be the most accurate, a non-sequential dataset is constructed from customer event histories by averaging features of the last few events. A simple K-nearest neighbor algorithm on this dataset is found to give significantly improved performance. It is quite intuitive to think that most people will react only to events in the fairly recent past. Events related to telecommunications occurring months or years ago are unlikely to have a large impact on a customer’s future behaviour, and these results bear this out. Methods that deal with sequential data also tend to be much more complex than those dealing with simple nontemporal data, giving an added benefit to expressing the recent information in a non-sequential manner

Neutron scattering in a d_{x^2-y^2}-wave superconductor with strong impurity scattering and Coulomb correlations

We calculate the spin susceptibility at and below T_c for a d_{x^2-y^2}-wave superconductor with resonant impurity scattering and Coulomb correlations. Both the impurity scattering and the Coulomb correlations act to maintain peaks in the spin susceptibility, as a function of momentum, at the Brillouin zone edge. These peaks would otherwise be suppressed by the superconducting gap. The predicted amount of suppression of the spin susceptibility in the superconducting state compared to the normal state is in qualitative agreement with results from recent magnetic neutron scattering experiments on La_{1.86}Sr_{0.14}CuO_4 for momentum values at the zone edge and along the zone diagonal. The predicted peak widths in the superconducting state, however, are narrower than those in the normal state, a narrowing which has not been observed experimentally.Comment: 24 pages (12 tarred-compressed-uuencoded Postscript figures), REVTeX 3.0 with epsf macros, UCSBTH-94-1

An intelligent assistant for exploratory data analysis

In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm