Insufficient coping behavior under chronic stress and vulnerability to psychiatric disorders

Epidemiological data indicate that 75% of subjects with major psychiatric disorders have their onset in the age range of 17-24 years. An estimated 35-50% of college and university students drop out prematurely due to insufficient coping skills under chronic stress, while 85% of students receiving a psychiatric diagnosis withdraw from college/university prior to the completion of their education. In this study we aimed at developing standardized means for identifying students with insufficient coping skills under chronic stress and at risk for mental health problems. A sample of 1,217 college students from 3 different sites in the U.S. and Switzerland completed 2 self-report questionnaires: the Coping Strategies Inventory "COPE" and the Zurich Health Questionnaire "ZHQ" which assesses "regular exercises", "consumption behavior", "impaired physical health", "psychosomatic disturbances", and "impaired mental health". The data were subjected to structure analyses by means of a Neural Network approach. We found 2 highly stable and reproducible COPE scales that explained the observed inter-individual variation in coping behavior sufficiently well and in a socio-culturally independent way. The scales reflected basic coping behavior in terms of "activity-passivity" and "defeatism-resilience", and in the sense of stable, socio-culturally independent personality traits. Correlation analyses carried out for external validation revealed a close relationship between high scores on the defeatism scale and impaired physical and mental health. This underlined the role of insufficient coping behavior as a risk factor for physical and mental health problems. The combined COPE and ZHQ instruments appear to constitute powerful screening tools for insufficient coping skills under chronic stress and for risks of mental health problems.

Association between education and quality of diabetes care in Switzerland

PURPOSE: Low socioeconomic status is associated with higher prevalence of diabetes, worse outcomes, and worse quality of care. We explored the relationship between education, as a measure of socioeconomic status, and quality of care in the Swiss context. PATIENTS AND METHODS: Data were drawn from a population-based survey of 519 adults with diabetes during fall 2011 and summer 2012 in a canton of Switzerland. We assessed patients and diabetes characteristics. Eleven indicators of quality of care were considered (six of process and five of outcomes of care). After bivariate analyses, regression analyses adjusted for age, sex, and diabetic complications were performed to assess the relationship between education and quality of care. RESULTS: Of 11 quality-of-care indicators, three were significantly associated with education: funduscopy (patients with tertiary versus primary education were more likely to get the exam: odds ratio, 1.8; 95% confidence interval [CI], 1.004-3.3) and two indicators of health-related quality of life (patients with tertiary versus primary education reported better health-related quality of life: Audit of Diabetes-Dependent Quality of Life: β=0.6 [95% CI, 0.2-0.97]; SF-12 mean physical component summary score: β=3.6 [95% CI, 0.9-6.4]). CONCLUSION: Our results suggest the presence of educational inequalities in quality of diabetes care. These findings may help health professionals focus on individuals with increased needs to decrease health inequalities.

Patterns of Subject Mix in Higher Education Institutions : A First Empirical Analysis Using the AQUAMETH Database

Teaching and research are organised differently between subject domains: attempts to construct typologies of higher education institutions, however, often do not include quantitative indicators concerning subject mix which would allow systematic comparisons of large numbers of higher education institutions among different countries, as the availability of data for such indicators is limited. In this paper, we present an exploratory approach for the construction of such indicators. The database constructed in the AQUAMETH project, which includes also data disaggregated at the disciplinary level, is explored with the aim of understanding patterns of subject mix. For six European countries, an exploratory and descriptive analysis of staff composition divided in four large domains (medical sciences, engineering and technology, natural sciences and social sciences and humanities) is performed, which leads to a classification distinguishing between specialist and generalist institutions. Among the latter, a further distinction is made based on the presence or absence of a medical department. Preliminary exploration of this classification and its comparison with other indicators show the influence of long term dynamics on the subject mix of individual higher education institutions, but also underline disciplinary differences, for example regarding student to staff ratios, as well as national patterns, for example regarding the number of PhD degrees per 100 undergraduate students. Despite its many limitations, this exploratory approach allows defining a classification of higher education institutions that accounts for a large share of differences between the analysed higher education institutions.

Contribution of physical education to overall physical activity.

For many children, physical activity (PA) during physical education (PE) lessons provides an important opportunity for being physically active. Although PA during PE has been shown to be low, little is known about the contribution of PA during PE to overall PA. The aim was therefore to assess children's PA during PE and to determine the contribution of PE to overall PA with special focus on overweight children. Accelerometer measurements were done in 676 children (9.3 ± 2.1 years) over 4-7 days in 59 randomly selected classes. Moderate-and-vigorous PA (MVPA; ≥ 2000 counts/min) during PE (MVPA(PE) ), overall MVPA per day (MVPA(DAY) ), and a comparison of days with and without PE were calculated by a regression model with gender, grade, and weight status (normal vs overweight) as fixed factors and class as a random factor. Children spent 32.8 ± 15.1% of PE time in MVPA. Weight status was not associated to MVPA(PE) . MVPA(PE) accounted for 16.8 ± 8.5% of MVPA(DAY) , and 17.5 ± 8.2% in overweight children. All children were more active on days with PE than on days without PE (differences: 16.1 ± 29.0 min of MVPA(DAY) ; P ≤ 0.001; 13.7 ± 28.0 min for overweight children). Although MVPA(PE) was low, PE played a considerable role in providing PA and was not compensated by reducing extracurricular MVPA.