31 research outputs found

    Struggling for access to appropriate healthcare services: A qualitative content analysis of patient complaints

    Get PDF
    Aim: This study aimed to describe circumstances concerning access for patients and relatives to take part in patient health and safety in a hospital setting. Design: This study used a qualitative descriptive design and was conducted at a Swedish university hospital. Method: The 79 complaints reported by patients and relatives included in this study were registered between January 2017 and June 2019. These complaints were classified as concerning access to healthcare services. Data were analysed using qualitative content analysis. Results: The overarching theme, struggling for access as a human being in the healthcare system, encompassed three themes describing patients\u27 and relatives\u27 needs. The three themes were (1) navigating through the healthcare organization, (2) making sense of self and what is going on and (3) being acknowledged as having needs. Conclusion: Patients and relatives continuously participate in various ways in healthcare to promote health and prevent patient harm. Our findings contribute important knowledge about the meaning of access from a broad healthcare system perspective. Access was restricted in terms of appropriateness in how patients\u27 needs were met. This restriction of access risked the deterioration of patient health and safety. Impact: Patients and relatives play an active part in patient health and safety, although their attempts are sometimes hindered. Restrictions in the appropriateness of access prevented patients and relatives from taking part in patient health and safety, which appeared to mean that they had to adapt and expend effort to the point that it negatively affected their health and everyday life. These findings concern all patients, relatives and healthcare professionals in hospital-associated settings. Patient or Public Contribution: No patient or public contribution

    Reactivity-based one-pot total synthesis of fucose GM(1) oligosaccharide: A sialylated antigenic epitope of small-cell lung cancer

    No full text
    The total synthesis of the sialic acid-containing antigenic epitope fucose GM(1) (Fuc-GM(1)) by an improved reactivity-based one-pot synthetic strategy is reported. Based on a thioglycoside reactivity database, three saccharide building blocks, 3, 4, and 5, were designed and prepared to incorporate a descending order of reactivity toward thiophilic activation. Using the reactivity-based one-pot synthetic method, the fully protected Fuc-GM(1) glycoside 2 was furnished in a facile manner, which was globally deprotected to give the Fuc-GM(1) glycoside 1. In addition, using the promoter system 1-(benzensulfinyl)piperidine/trifluoromethanesulfonic anhydride, the product yield was improved and the reaction time was reduced in comparison with the N-iodosuccinimide/trifluoromethanesulfonic acid- and dimethyl (thiomethyl) sulfonium trifluoromethanesulfonate-promoted systems

    Predicting regional COVID-19 hospital admissions in Sweden using mobility data

    Get PDF
    The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.Funding Agencies|Chalmers University of Technology</p
    corecore