Good practice in health care for migrants: views and experiences of care professionals in 16 European countries

<p>Abstract</p> <p>Background</p> <p>Health services across Europe provide health care for migrant patients every day. However, little systematic research has explored the views and experiences of health care professionals in different European countries. The aim of this study was to assess the difficulties professionals experience in their service when providing such care and what they consider constitutes good practice to overcome these problems or limit their negative impact on the quality of care.</p> <p>Methods</p> <p>Structured interviews with open questions and case vignettes were conducted with health care professionals working in areas with high proportion of migrant populations in 16 countries. In each country, professionals in nine primary care practices, three accident and emergency hospital departments, and three community mental health services (total sample = 240) were interviewed about their views and experiences in providing care for migrant patients, i.e. from first generation immigrant populations. Answers were analysed using thematic content analysis.</p> <p>Results</p> <p>Eight types of problems and seven components of good practice were identified representing all statements in the interviews. The eight problems were: language barriers, difficulties in arranging care for migrants without health care coverage, social deprivation and traumatic experiences, lack of familiarity with the health care system, cultural differences, different understandings of illness and treatment, negative attitudes among staff and patients, and lack of access to medical history. The components of good practice to overcome these problems or limit their impact were: organisational flexibility with sufficient time and resources, good interpreting services, working with families and social services, cultural awareness of staff, educational programmes and information material for migrants, positive and stable relationships with staff, and clear guidelines on the care entitlements of different migrant groups. Problems and good care components were similar across the three types of services.</p> <p>Conclusions</p> <p>Health care professionals in different services experience similar difficulties when providing care to migrants. They also have relatively consistent views on what constitutes good practice. The degree to which these components already are part of routine practice varies. Implementing good practice requires sufficient resources and organisational flexibility, positive attitudes, training for staff and the provision of information.</p

ThomX - Conceptual Design Report

A. Variola, A. Loulergue, F. Zomer (eds.

Concerted action of two cation filters in the aquaporin water channel

Aquaporin (AQP) facilitated water transport is common to virtually all cell membranes and is marked by almost perfect specificity and high flux rates. Simultaneously, protons and cations are strictly excluded to maintain ionic transmembrane gradients. Yet, the AQP cation filters have not been identified experimentally. We report that three point mutations turned the water-specific AQP1 into a proton/alkali cation channel with reduced water permeability and the permeability sequence: H+ ≫K+ >Rb+ >Na+ >Cs+ >Li+. Contrary to theoretical models, we found that electrostatic repulsion at the central asn-pro-ala (NPA) region does not suffice to exclude protons. Full proton exclusion is reached only in conjunction with the aromatic/arginine (ar/R) constriction at the pore mouth. In contrast, alkali cations are blocked by the NPA region but leak through the ar/R constriction. Expression of alkali-leaking AQPs depolarized membrane potentials and compromised cell survival. Our results hint at the alkali-tight but solute-unselective NPA region as a feature of primordial channels and the proton-tight and solute-selective ar/R constriction variants as later adaptations within the AQP superfamily

Machine Learning Predicts Laboratory Earthquakes

Forecasting fault failure is a fundamental but elusive goal in earthquake science. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal, and do not make use of its history. Surprisingly, machine learning identifies a signal emitted from the fault zone previously thought to be low-amplitude noise that enables failure forecasting throughout the laboratory quake cycle. We hypothesize that applying this approach to continuous seismic data may lead to significant advances in identifying currently unknown signals, in providing new insights into fault physics, and in placing bounds on fault failure times.Comment: 17 pages, 4 figure