Gender, school and academic year differences among Spanish university students at high-risk for developing an eating disorder: An epidemiologic study

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to assess the magnitude of the university population at high-risk of developing an eating disorder and the prevalence of unhealthy eating attitudes and behaviours amongst groups at risk; gender, school or academic year differences were also explored.</p> <p>Methods</p> <p>A cross-sectional study based on self-report was used to screen university students at high-risk for an eating disorder. The sample size was of 2551 university students enrolled in 13 schools between the ages of 18 and 26 years. The instruments included: a social-demographic questionnaire, the Eating Disorders Inventory (EDI), the Body Shape Questionnaire (BSQ), the Symptom Check List 90-R (SCL-90-R), and the Self-Esteem Scale (RSE). The sample design is a non-proportional stratified sample by academic year and school. The prevalence rate was estimated controlling academic year and school. Logistic regression analysis was used to investigate adjusted associations between gender, school and academic year.</p> <p>Results</p> <p>Female students presented unhealthy weight-control behaviours as dieting, laxatives use or self-induced vomiting to lose weight than males. A total of 6% of the females had a BMI of 17.5 or less or 2.5% had amenorrhea for 3 or more months. In contrast, a higher proportion of males (11.6%) reported binge eating behaviour. The prevalence rate of students at high-risk for an eating disorder was 14.9% (11.6–18) for males and 20.8% (18.7–22.8) for females, according to an overall cut-off point on the EDI questionnaire. Prevalence rates presented statistically significant differences by gender (p < 0.001) but not by school or academic year.</p> <p>Conclusion</p> <p>The prevalence of eating disorder risk in university students is high and is associated with unhealthy weight-control practices, similar results have been found in previous studies using cut-off points in questionnaires. These results may be taken into account to encourage early detection and a greater awareness for seeking treatment in order to improve the diagnosis, among students on university campuses.</p

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

The EAT-26 as screening instrument for clinical nutrition unit attenders

n/

The Effect of Time Spent Online on Student Achievement in Online Economics and Finance Courses

This article studies the determinants of academic achievement in online courses in economics and finance. The authors use the online tracking feature in Blackboard (Campus Edition) to retrieve the real time that each student spent in the course for the entire semester and to analyze the impact of time spent online, prior grade point average (GPA), and some demographic characteristics of students on their final grades. Both time and GPA are significant determinants of the final grade: Higher GPAs and longer times spent online are associated with higher grades