998 resultados para Winthrop College
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by Herbert Loewe
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Introduction. A vast majority of studies conducted in both developed and developing nations have focused on the epidemiology of HBV (Hepatitis B virus) and HCV (Hepatitis C virus) in high-risk populations; low-risk populations have been neglected. Recently Hwang et al conducted a unique large cross-sectional study in American university students that focused on cosmetic procedures and drug use for acquiring these infections among a low-risk young adult population In Houston. ^ Methods. This study is a secondary data analysis of the cross-sectional study conducted by Hwang et al. Data for this anonymous study were collected from 7,960 college students, among whom were the 2,561 non US/Canadian born students included in this study. All students completed a self-administered questionnaire and provided a blood sample. The epidemiology of HBV/HCV and risk factors for acquiring HBV/HCV infection was studied by comparing those with HBV/HCV infection versus those without. Both univariate and multivariate logistic regression was used to analyze the data. ^ Results. Overall prevalence of HBV and HCV infections were 22% and 0.8% respectively. By multivariable analysis, the factors that were independently associated with increased prevalence of HBV infection were increasing age per year (OR=1.06, 95% C.I=1.04-1.08), Black or Asian race (OR=6.21, 95% C.I=3.14-12.27), history of household contact with hepatitis (OR=1.87, 95% C.I=1.15-3.05), and having sexual partner with hepatitis (OR=5.20, 95% C.I=1.5-18.00). For HCV these factors included increasing age per year (OR= 1.08, 95% C.I=1.03-1.14), history of blood transfusion prior to 1991 (OR=25.45, 95% C.I=7.58-85.40), and Injection drug use. (OR=78.15, 95% C.I=12.19-500.85). Cosmetic procedures like tattooing were not significant risk factors for either HBV or HCV infection. ^ Conclusions. In a low-risk adult foreign born population, cosmetic procedures are not significant risk factors for HBV or HCV infection. The prevention strategies of these infections in this population should focus on safe sexual practices/abstinence and HBV vaccination should be provided to adolescents and sexually active adults. ^
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Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^
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Built on a free, bilingual, "high-touch, high-tech" platform, New Futuro has created a robust community of Latino students and parents, non-profit organizations, education institutions, government agencies, and corporations to connect those that need help with those that provide it. One of the resources developed by New Futuro is a proprietary 10-Steps College Plan that provides structured information targeted to Latino students and families to help them prepare, apply and pay for college.
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In 1998, Texas initiated a bold new statewide university admission policy aimed at increasing college access for traditionally underserved students in the state. House Bill 588 (known as the Texas Top 10 Percent Plan (TTPP)) guaranteed automatic admission to the college or university of their choice for all top performing students in Texas public high schools. Fourteen years after the plan’s implementation, we see great strides and complexities in understanding student outcomes as a result of the percent plan. However, the legal controversy over the percent plan both in Texas and other states incorporating similar yet distinctly motivated alternative admissions plans continues to play out from institutional decision boards to the highest court in the nation. This study seeks to add to that discussion by exploring two questions. Descriptively, what are the admission and enrollment patterns within racial/ethnic groups of percent plan eligible students, over time, for Texas elite, emergent elite, and remaining public institutions? Given that all eligible percent plan students may enter the institution of choice in Texas, does which type of institution a TTPP student chooses relate to their race/ethnicity? The descriptive story told by the admission and enrollment distributions of equally eligible TTPP students is a complex but compelling one. Fundamentally, it identifies that statistically different application and enrollment patterns exist for Hispanic and especially African American TTPP beneficiaries relative to their White and Asian American counterparts.
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Invited commentary on "When Policy Opportunity is not Enough: College Access and Enrollment Patterns among Texas Percent Plan Eligible Students" by Catherine Horn and Stella Flores.
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Existing data, collected from 1st-year students enrolled in a major Health Science Community College in the south central United States, for Fall 2010, Spring 2011, Fall 2011 and Spring 2012 semesters as part of the "Online Navigational Assessment Vehicle, Intervention Guidance, and Targeting of Risks (NAVIGATOR) for Undergraduate Minority Student Success" with CPHS approval number HSC-GEN-07-0158, was used for this thesis. The Personal Background and Preparation Survey (PBPS) and a two-question risk self-assessment subscale were administered to students during their 1st-year orientation. The PBPS total risk score, risk self-assessment total and overall scores, and Under Representative Minority Student (URMS) status were recorded. The purpose of this study is to evaluate and report the predictive validity of the indicators identified above for Adverse Academic Status Events (AASE) and Nonadvancement Adverse Academic Status Events (NAASE) as well as the effectiveness of interventions targeted using the PBPS among a diverse population of health science community college students. The predictive validity of the PBPS for AASE has previously been demonstrated among health science professions and graduate students (Johnson, Johnson, Kim, & McKee, 2009a; Johnson, Johnson, McKee, & Kim, 2009b). Data will be analyzed using binary logistic regression and correlation using SPSS 19 statistical package. Independent variables will include baseline- versus intervention-year treatments, PBPS, risk self-assessment, and URMS status. The dependent variables will be binary AASE and NAASE status. ^ The PBPS was the first reliable diagnostic and prescriptive instrument to establish documented predictive validity for student Adverse Academic Status Events (AASE) among students attending health science professional schools. These results extend the documented validity for the PBPS in predicting AASE to a health science community college student population. Results further demonstrated that interventions introduced using the PBPS were followed by approximately one-third reduction in the odds of Nonadvancement Adverse Academic Status Events (NAASE), controlling for URMS status and risk self-assessment scores. These results indicate interventions introduced using the PBPS may have potential to reduce AASE or attrition among URMS and nonURMS attending health science community colleges on a broader scale; positively impacting costs, shortages, and diversity of health science professionals.^
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Fil: Jalif de Bertranou, Clara Alicia.