OBJECTIVES: To analyse the attitudes of medical personnel towards terminally ill patients and their right to be fully informed. DESIGN: Self-administered questionnaire composed of 56 closed questions. SETTING: Three general hospitals and eleven health centres in Granada (Spain). The sample comprised 168 doctors and 207 nurses. RESULTS: A high percentage of medical personnel (24.1%) do not think that informing the terminally ill would help them face their illness with greater serenity. Eighty-four per cent think the patient's own home is the (...) best place to die: 8.9% of the subjects questioned state that the would not like to be informed of an incurable illness. CONCLUSION: In our opinion any information given should depend on the patient's personality, the stage of the illness and family circumstances. Our study confirms that a hospital is not the ideal environment for attending to the needs of the terminally ill and their families. (shrink)

University program dropout is a problem that has important consequences not only for the student that leaves but also for the institution in which the withdrawal occurs. Therefore, higher education institutions must study the problem in greater depth to establish appropriate prevention measures in the future. However, most research papers currently focus primarily on the characteristics of students who leave university, rather than on those who choose to pursue alternative courses of study and therefore fail to take into account the (...) different kinds of abandonment. The aim of this paper is to identify the different types of dropout to define their characteristics and propose some recommendations. Thus, an ex post facto study was carried out on a sample of 1,311 freshmen from a university in the north of Spain using data gathered using an ad-hoc designed questionnaire, applied by telephone or an online survey, and completed with data available in the university data warehouse. A descriptive analysis was performed to characterize the sample and identify five different groups, including 1. Students persisting in their initiated degree 2. Students who change of program (within the same university) 3. Students transferring to a different university 4. Students enrolling in non-higher-education studies 5. Students that quit studying. Also, data mining techniques (decision trees) were applied to classify the cases and generate predictive models to aid in the design of differentiated intervention strategies for each of the corresponding groups. (shrink)