El afianzamiento de las competencias sistémicas e instrumentales en el alumno universitario a través de la enseñanza práctica del Sistema Constitucional Español de Protección de los Derechos y Libertades

La adaptación de los estudios universitarios al Espacio Europeo de Educación Superior implica, además del tradicional aprendizaje de contenidos técnicos sobre una materia, la adquisición de competencias sistémicas, instrumentales e interpersonales. La enseñanza práctica del Derecho Constitucional, y, concretamente, del sistema constitucional español de protección de los derechos fundamentales y las libertades públicas, afianza, sin duda alguna, dichas competencias en el estudiante universitario.The adaptation of university studies at the EEES involves, in addition to the traditional learning content on a technical matter, the acquisition of systemic, instrumental and interpersonal skills. The practice teaching of Constitutional Law, and specifically the Spanish constitutional system of protection of fundamental rights and public freedoms, strengthens undoubtedly those skills in the university student

El afianzamiento de las competencias sistémicas e instrumentales en el alumno universitario a través de la enseñanza práctica del sistema constitucional español de protección de los derechos y libertades

La adaptación de los estudios universitarios al Espacio Europeo de Educación Superior implica, además del tradicional aprendizaje de contenidos técnicos sobre una materia, la adquisición de competencias sistémicas, instrumentales e interpersonales. La enseñanza práctica del Derecho Constitucional, y, concretamente, del sistema constitucional español de protección de los derechos fundamentales y las libertades públicas, afianza, sin duda alguna, dichas competencias en el estudiante universitario

The Interpretation and application of the law with a gender perspective in Spain: challenges and outstanding issues

This work, situated within the Spanish legal context, presents a reflection upon the need to apply and interpret the law bearing in mind the mandate of the gender perspective contained in national and international legislation. In spite of the significant advances, in questions of gender equality, evidenced by the Spanish legal system in recent decades, these have not led to real, effective equality. In the spheres of both private and public law, this task should be prioritised and undertaken in transversal fashion by all legal operators, judges in particular. Without this commitment, the guarantee of women’s human rights will continue to be hindered by the shortcomings of an androcentric legal culture that perpetuates structural discrimination against women

Identification of high-risk patients for referral through machine learning assisting the decision making to manage minor ailments in community pharmacies

Background: Data analysis techniques such as machine learning have been used for assisting in triage and the diagnosis of health problems. Nevertheless, it has not been used yet to assist community pharmacists with services such as the Minor Ailment Services These services have been implemented to reduce the burden of primary care consultations in general medical practitioners (GPs) and to allow a better utilization of community pharmacists’ skills. However, there is a need to refer high-risk patients to GPs.Aim: To develop a predictive model for high-risk patients that need referral assisting community pharmacists’ triage through a minor ailment service.Method: An ongoing pragmatic type 3 effectiveness-implementation hybrid study was undertaken at a national level in Spanish community pharmacies since October 2020. Pharmacists recruited patients presenting with minor ailments and followed them 10 days after the consultation. The main outcome measured was appropriate medical referral (in accordance with previously co-designed protocols). Nine machine learning models were tested (three statistical, three black box and three tree models) to assist pharmacists in the detection of high-risk individuals in need of referral.Results: Over 14′000 patients were included in the study. Most patients were female (68.1%). With no previous treatment for the specific minor ailment (68.0%) presented. A percentage of patients had referral criteria (13.8%) however, not all of these patients were referred by the pharmacist to the GP (8.5%). The pharmacists were using their clinical expertise not to refer these patients. The primary prediction model was the radial support vector machine (RSVM) with an accuracy of 0.934 (CI95 = [0.926,0.942]), Cohen’s kappa of 0.630, recall equal to 0.975 and an area under the curve of 0.897. Twenty variables (out of 61 evaluated) were included in the model. radial support vector machine could predict 95.2% of the true negatives and 74.8% of the true positives. When evaluating the performance for the 25 patient’s profiles most frequent in the study, the model was considered appropriate for 56% of them.Conclusion: A RSVM model was obtained to assist in the differentiation of patients that can be managed in community pharmacy from those who are at risk and should be evaluated by GPs. This tool potentially increases patients’ safety by increasing pharmacists’ ability to differentiate minor ailments from other medical conditions