Locus of control as a predictor of students dropping out of high school

Education is related to health. In cross-sectional data, education level has been associated with physical functioning. Also, lower levels of education have been associated with health behaviors including smoking, alcohol use, and greater body weight. In school, students may benefit from greater exposed to health-related messages, while students who have dropped out may be more susceptible to influences regarding negative health behaviors such as smoking. ^ Improved school retention might improve long-term health outcomes. However, there is limited evidence regarding modifiable factors that predict likelihood of dropping out. Two likely psychosocial measures are locus of control and parent-child academic conversations. In the current study, data from two waves of a population-based longitudinal survey, the National Education Longitudinal Survey, were utilized to evaluate whether these two psychosocial measures could predict likelihood of dropping out, for students (n = 16,749) in tenth grade at 1990, with dropout status determined at 1992, while controlling for recognized sociodemographic predictors including parental income, parental education level, race/ethnicity, and sex. Locus of control was measured with the Pearlin Mastery Scale, and parent-child academic conversations were measured by three questions concerning course selection at school, school activities and events, and things the student studied in class. ^ In a logistic regression model, with the sociodemographic control measures entered in a first step before entry of the psychosocial measures in a second step, this study determined that lower levels of locus of control were associated with greater likelihood of dropping out after two years (odds ratio (OR) = 1.11, 95% confidence interval (CI) 108 to 1.15, p < .001), and two of the three parent-child academic discussion items were associated with greater likelihood of dropping out after two years (OR = 1.69, CI 1.48-1.93, p < .001; OR = 1.22, CI 1.05-1.41, p = .01; OR = 1.01, CI .88-1.15, p = .94). ^ It is possible that interventions aimed at improving locus of control, and aimed at building parent-child academic conversations, could lower the likelihood of students dropping out, and this in turn could yield improved heath behaviors and health status in the child's future. ^

Sleep quality and quantity and associated factors among high school students from south Texas

Background. Insufficient and poor quality sleep among adolescents affects not only the cognitive functioning, but overall health of the individual. Existing research suggests that adolescents from varying ethnic groups exhibit differing sleep patterns. However, little research focuses on sleep patterns and associated factors (i.e. tobacco use, mental health indicators) among Hispanic youth. ^ Methods. The study population (n=2,536) included students in grades 9-12 who attended one of the three public high schools along the Texas-Mexico border in 2003. This was a cross sectional study using secondary data collected via a web-based, confidential, self-administered survey. Separate logistic regression models were estimated to identify factors associated with reduced (<9 hours/night) and poor quality sleep on average during weeknights. ^ Results. Of participants, 49.5% reported reduced sleep while 12.8% reported poor quality sleep. Factors significantly (p<0.05) associated with poor quality sleep were: often feeling stressed or anxious (OR=5.49), being born in Mexico (OR=0.65), using a computer/playing video games 15+ hours per week (OR=2.29), working (OR=1.37), being a current smoker (OR=2.16), and being a current alcohol user (OR=1.64). Factors significantly associated with reduced quantity of sleep were: often feeling stressed or anxious (OR=2.74), often having headaches/stomachaches (OR=1.77), being a current marijuana user (OR=1.70), being a current methamphetamine user (OR=4.92), and being a current alcohol user (OR=1.27). ^ Discussion. Previous research suggests that there are several factors that can influence sleep quality and quantity in adolescents. This paper discusses these factors (i.e. work, smoking, alcohol, etc.) found to be associated with poor sleep quality and reduced sleep quantity in the Hispanic adolescent population. A reduced quantity of sleep (81.20% of the participants) and a poor quality of sleep (12.80% of the participants) were also found in high school students from South Texas. ^

The impact of traditional psychiatric services and case management services on the functioning of people with schizophrenia: A cross-national study in Nova Scotia and Texas

The purpose of this study was to determine the impact of traditional psychiatric services with case management services on the functioning of people with schizophrenia. Traditional services were defined as routine clinic services consisting of medication follow-along, psychotherapy, and support services. Case management consisted of activities involved in linking, planning, and monitoring services for the outpatient client who has schizophrenia. The target population was adult schizophrenics who had been receiving outpatient clinic services for a minimum of six months. Structured interviews were conducted using standardized scales (e.g., Quality of Life, Self-Efficacy, and Brief Symptom Inventory) with 78 outpatient client volunteers from two sites: Nova Scotia (Canada) and Texas (USA). The researcher tested for differences in psychiatric symptomatology, recidivism, and quality of life for persons with schizophrenia receiving traditional psychiatric services in Nova Scotia and traditional plus case management services in Texas. Data were collected from the structured interviews and medical records review forms. Types of services were blocked into low and high levels of Intensity (frequency x minutes) and compared to determine the relative contribution of each. Finally, the role of clients' self-efficacy was tested as an intervening variable. Although the findings did not support the hypotheses in the direction anticipated, there were some interesting and useful results. From the Nova Scotia site, clients who received low levels of services were hospitalized less compared to the Texas site. The more psychotic a patient was the higher their involvement in medication follow-along and the more monitoring they received. The more psychotherapy received, the lower the reported satisfaction with social relationships. Of particular interest is the role that self-efficacy played in improved client outcomes. Although self-efficacy scores were related to improved functioning, the mechanism for this still needs to be clarified through subsequent research. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.