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Data from over 21,600 students who left Catalan higher education institutions during the academic years 2000-2001 and 2001-2002 have been analysed in order to describe the academic and personal profiles of university dropouts. Additionally, a telephone survey and face-to-face interviews with a pilot group of leavers were conducted to gather additional qualitative information about the reasons for their decision. The influences on non-completion can be reduced to three main factors, among which dissatisfaction with the quality of the students' experience, family and work responsibilities as well as economic difficulties, are prominent. Analysis conducted showed no significant differences between Catalan higher education institutions, with dropout percentages ranging from 28% to 33%. These rates should be understood within a context of broad access to higher education. Within the European Higher Education Area scenario, the findings have triggered institutional endeavours to improve the quality of the students' and teachers' experience.

Data from over 21,600 students who left Catalan higher education institutions during the academic years 2000–2001 and 2001–2002 have been analysed in order to describe the academic and personal profiles of university dropouts. Additionally, a telephone survey and face-to-face interviews with a pilot group of leavers were conducted to gather additional qualitative information about the reasons for their decision. The influences on non-completion can be reduced to three main factors, among which dissatisfaction with the quality of the students’ experience, family and work responsibilities as well as economic difficulties, are prominent. Analysis conducted showed no significant differences between Catalan higher education institutions, with dropout percentages ranging from 28% to 33%. These rates should be understood within a context of broad access to higher education. Within the European Higher Education Area scenario, the findings have triggered institutional endeavours to improve the quality of the students’ and teachers’ experience. [ABSTRACT FROM PUBLISHER]

BackgroundPredictD is a risk algorithm that was developed to predict risk of onset of major depression over 12 months in general practice attendees in Europe and validated in a similar population in Chile. It was the first risk algorithm to be developed in the field of mental disorders. Our objective was to extend predictD as an algorithm to detect people at risk of major depression over 24 months.MethodParticipants were 4190 adult attendees to general practices in the UK, Spain, Slovenia and Portugal, who were not depressed at baseline and were followed up for 24 months. The original predictD risk algorithm for onset of DSM-IV major depression had already been developed in data arising from the first 12 months of follow-up. In this analysis we fitted predictD to the longer period of follow-up, first by examining only the second year (12–24 months) and then the whole period of follow-up (0–24 months).ResultsThe instrument performed well for prediction of major depression from 12 to 24 months [c-index 0.728, 95% confidence interval (CI) 0.675–0.781], or over the whole 24 months (c-index 0.783, 95% CI 0.757–0.809).ConclusionsThe predictD risk algorithm for major depression is accurate over 24 months, extending it current use of prediction over 12 months. This strengthens its use in prevention efforts in general medical settings. [ABSTRACT FROM AUTHOR]

BackgroundThe different incidence rates of, and risk factors for, depression in different countries argue for the need to have a specific risk algorithm for each country or a supranational risk algorithm. We aimed to develop and validate a predictD-Spain risk algorithm (PSRA) for the onset of major depression and to compare the performance of the PSRA with the predictD-Europe risk algorithm (PERA) in Spanish primary care.MethodA prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multi-level logistic regression and inverse probability weighting to build the PSRA. In Spain (4574), Chile (2133) and another five European countries (5184), 11 891 non-depressed adult primary care attendees formed our at-risk population. The main outcome was DSM-IV major depression (CIDI).ResultsSix variables were patient characteristics or past events (sex, age, sex×age interaction, education, physical child abuse, and lifetime depression) and six were current status [Short Form 12 (SF-12) physical score, SF-12 mental score, dissatisfaction with unpaid work, number of serious problems in very close persons, dissatisfaction with living together at home, and taking medication for stress, anxiety or depression]. The C-index of the PSRA was 0.82 [95% confidence interval (CI) 0.79–0.84]. The Integrated Discrimination Improvement (IDI) was 0.0558 [standard error (s.e.)=0.0071, Zexp=7.88, p<0.0001] mainly due to the increase in sensitivity. Both the IDI and calibration plots showed that the PSRA functioned better than the PERA in Spain.ConclusionsThe PSRA included new variables and afforded an improved performance over the PERA for predicting the onset of major depression in Spain. However, the PERA is still the best option in other European countries. [ABSTRACT FROM PUBLISHER]

BackgroundThere are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.MethodFamily practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.ResultsThere were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.ConclusionsPredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression. [ABSTRACT FROM PUBLISHER]

Summary: The spatial variability through time of the degree of salinity of soils and groundwater is a major issue concerning the sustainable management of water resources. The problem is accentuated in coastal plain areas where saline bodies of different origin coexist. We present an extensive geoelectrical resistivity dataset collected in the Alt Empordà clastic coastal aquifer system (NE Spain) integrating the results of field campaigns undertaken within a period of more than two decades. The dataset is unique in the literature and comprises highly valuable information for the investigation of saline domains. The joint application of Vertical Electrical Sounding (VES) and Electrical Resistivity Tomography (ERT) methods allowed the identification of saline domains and monitoring their evolution through time. Geophysical data gathered during field campaigns in 1982, 2002 and 2010 were calibrated and interpreted on the basis of soil and groundwater physico-chemical analyses and hydrogeological, stratigraphic and geomorphological data. The results illustrate the potential of electrical resistivity methods at differentiating saline domains in coastal aquifer areas and at modelling their configuration and evolution. Although the VES data processing allows solving the one-dimensional resistivity structure of the subsurface, the inversion imposes a 1D layered model, thus interpretation of two-dimensional structures is subject to the interpolation between discrete measures. In contrast, ERT data offer a continuous 2D-image of the resistivity distribution, both laterally and in depth. Given that the bulk resistivity of the medium is extremely sensitive to salt content, the ERT profiles permitted identifying and determining the geometry of hyper-saline areas (characterized by resistivities of less than 5Ωm). The study demonstrates the efficiency of the electrical methods to map subsurface conductive zones and illustrates how the accurate knowledge of the hydrogeological pattern results critical to meet this objective. [Copyright &y& Elsevier]

Background: There is evidence of negative attitudes among health professionals towards people with mental illness but there is also a knowledge gap on what training must be given to these health professionals during their education. The purpose of this study is to compare the attitudes of students of health sciences: nursing, medical, occupational therapy, and psychology., Methods: A comparative and cross-sectional study in which 927 final-year students from health sciences university programmes were evaluated using the Mental Illness: Clinicians' Attitudes (both MICA-2 and MICA-4) scale. The sample was taken in six universities from Chile and Spain., Results: We found consistent results indicating that stigma varies across university programmes. Medical and nursing students showed more negative attitudes than psychology and occupational therapy students in several stigma-related themes: recovery, dangerousness, uncomfortability, disclosure, and discriminatory behaviour., Conclusions: Our study presents a relevant description of the attitudes of each university programme for education against stigma in the formative years. Results show that the biomedical understanding of mental disorders can have negative effects on attitudes, and that education based on the psychosocial model allows a more holistic view of the person over the diagnosis.