356,663 research outputs found
Association between SARS-CoV-2 Seroprevalence in Nursing Home Staff and Resident COVID-19 Cases and Mortality: A Cross-Sectional Study
No full textThe burden of COVID-19 has disproportionately impacted the elderly, who are at increased risk of severe disease, hospitalization, and death. This cross-sectional study aimed to assess the association between SARS-CoV-2 seroprevalence among nursing home staff, and cumulative incidence rates of COVID-19 cases, hospitalizations, and deaths among residents. Staff seroprevalence was estimated within the SEROCoV-WORK+ study between May and September 2020 across 29 nursing homes in Geneva, Switzerland. Data on nursing home residents were obtained from the canton of Geneva for the period between March and August 2020. Associations were assessed using Spearman’s correlation coefficient and quasi-Poisson regression models. Overall, seroprevalence among staff ranged between 0 and 31.4%, with a median of 8.3%. A positive association was found between staff seroprevalence and resident cumulative incidence of COVID-19 cases (correlation coefficient R = 0.72, 95%CI 0.45–0.87; incidence rate ratio [IRR] = 1.10, 95%CI 1.07–1.17), hospitalizations (R = 0.59, 95%CI 0.25–0.80; IRR = 1.09, 95%CI 1.05–1.13), and deaths (R = 0.71, 95%CI 0.44–0.86; IRR = 1.12, 95%CI 1.07–1.18). Our results suggest that SARS-CoV-2 transmission between staff and residents may contribute to the spread of the virus within nursing homes. Awareness among nursing home professionals of their likely role in the spread of SARS-CoV-2 has the potential to increase vaccination coverage and prevent unnecessary deaths due to COVID-19
Psychometric properties of Work Ability Index in the light of comparative survey study
No full textThis article is focused on psychometric properties of Work Ability Index. The authors
have undertaken this task in order to check whether WAI may be considered as a reliable, valid and universal measurement of ability to work in the nursing profession. As an empirical basis authors used the data set obtained from Nurses’ Early Exit Study—coming from 10 European countries, an
extensive international survey research, conducted on almost 40,000 nurses. As a measure of work ability, WAI may be treated as internally coherent, although one item seems to be meaningless and unnecessary. Additionally, WAI should be considered a very predictive and cross-nationally stable instrument
The role of cumulative physical work load in symptomatic knee osteoarthritis : a case-control study in Germany
No full textObjectives To examine the dose-response relationship between cumulative exposure to kneeling and squatting as well as to lifting and carrying of loads and symptomatic knee osteoarthritis (OA) in a population-based case-control study. Methods In five orthopedic clinics and five practices we recruited 295 male patients aged 25 to 70 with radiographically confirmed knee osteoarthritis associated with chronic complaints. A total of 327 male control subjects were recruited. Data were gathered in a structured personal interview. To calculate cumulative exposure, the self-reported duration of kneeling and squatting as well as the duration of lifting and carrying of loads were summed up over the entire working life. Results The results of our study support a dose-response relationship between kneeling/squatting and symptomatic knee osteoarthritis. For a cumulative exposure to kneeling and squatting > 10.800 hours, the risk of having radiographically confirmed knee osteoarthritis as measured by the odds ratio (adjusted for age, region, weight, jogging/athletics, and lifting or carrying of loads) is 2.4 (95% CI 1.1-5.0) compared to unexposed subjects. Lifting and carrying of loads is significantly associated with knee osteoarthritis independent of kneeling or similar activities. Conclusions As the knee osteoarthritis risk is strongly elevated in occupations that involve both kneeling/squatting and heavy lifting/carrying, preventive efforts should particularly focus on these "high-risk occupations"
A SARS-CoV-2 omicron (B.1.1.529) variant outbreak in a primary school in Geneva, Switzerland.
No full textUnity through Diversity: Value-in-Diversity Beliefs, Work Group Diversity, and Group Identification
No full textResearch on work group diversity has more or less neglected the possibility that reactions to diversity may be informed by individuals' beliefs about the value of diversity (vs. homogeneity) for their work group. We studied the role of such diversity beliefs as a moderator of the relationship between work group diversity and individuals' identification with the work group across two studies. Study 1 was a cross-sectional survey that focused on gender diversity and gender diversity beliefs. Study 2 was a laboratory experiment in which work group diversity and diversity beliefs were manipulated. Results of both studies support the prediction that work group diversity and group identification are more positively related the more individuals believe in the value of diversitydiversity;identification;social identity;self-categorization;value-in-diversity
Promotion of work ability among French health care workers: value of the work ability index
No full textThe French HCWs who have been studied belong to the French sample of European NEXT Study. This sample consists of 55 public and private institutions in five regions. WAI has been calculated on 4306 subjects out of 5376 because of missing data. More than a third have a moderate or a poor index (29.4% and 5.2%, respectively). Half (50.6%) of the sample have a good index and only 14.9%, an excellent one. The WAI decreases with age: 19.4% of the "less than 30", 14.6% of the 30-44 years old and 12.3% of the 45 + have an excellent WAI. But, the rate of decrease depends definitely on job demands and especially on physical load. The multivariate analysis showed that in France as well as in the other countries, work demand, uncertainty about treatments, low support from colleagues, and dissatisfaction with psychological support had high odds ratios for a low WAI. Dissatisfaction with physical working conditions and the necessity to maintain uncomfortable postures were the second group of factors with a strong influence on a low WAI. The absence of time for sports or leisure remained strongly linked with a low WAI after an adjustment to the other risk factors. The WAI enables the workplace physician to summarize data which the hospital can use to further its thinking about how to manage jobs and skills
Group work assessment: benefits, problems and implications for good practice
No full textGroup work has become increasingly important in higher education, largely as a result of the greater emphasis on skills, employability and lifelong learning. However, it is often introduced in a hurry, can be unsupported and may be assessed without fully exploring the consequences (www.heacademy.ac.uk/ourwork/learning/assessment.group). Both group work and its assessment have been the focus of considerable research and debate in the higher education literature; see for example reviews by Webb (1994), Nightingale et al. (1996) and Boud et al. (1999). Davis (1993) identifies three types of group work: formal learning groups, informal learning groups and study groups. Formal groups are established to complete a specific task in one class session or over many weeks, such as a laboratory experiment or the compilation of an environmental impact report. Informal groups involve ad hoc clusters of students who work in class to discuss an issue or test understanding. Study teams are formed to provide support for members, usually for the duration of a project or module. This guide will focus on formal group activity and its assessment. Group work is highly complex, however, and assessment should consider both the product or outcome and the process of student learning (Webb 1994, Glebhill and Smith 1996). Consequently, the development of effective group work assessment strategies, designed to engage the students and provide the best possible learning experience, raises a number of important questions. For example, what is the most effective group size? How should the groups be formed? How can we best prepare students for group work? What are the most effective ways of supporting groups and individuals within them? To what extent should group progress be monitored by tutors? How should we assess group work and where does the balance lie between product and process, and group and individual? What is the most effective way of gathering meaningful student feedback for 2 the purposes of evaluation and review? This guide will explore these questions and many others. It will begin by looking at the benefits of group work and its assessment before exploring some of the key concerns. It will then reflect on some personal experiences and lessons learned from the planning and delivery of group work assessment strategies, with a view to providing some ideas and tips for good practice
Towards a better understanding of group forking dynamics in virtual contexts
No full textGroup Fork" is defined as more than two group members leave their parent organization and start a new group. While group fork is a common social phenomenon in any type of group, it is still understudied in virtual contexts. Drawing upon the literature from three fields, religious research, social psychology and organization studies, this study attempts to bridge this gap by answering two questions, "what causes group fork?" and how is individual dissatisfaction transformed into group-level dissatisfaction in virtual contexts, thus leading to the eventual fork? Free/Libre Open Source Software (FLOSS) projects will be used as examples of self-organizing virtual work, as they provide a good context to observe the whole process of how group interactions are intertwined toward to the eventual fork. A multi-stage research strategy is conducted in this study and preliminary findings will be reported.
Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data
No full textDiffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample
Modelling solutions to the impact of COVID-19 on cardiovascular waiting Lists
No full textThis three-day virtual study group explored the challenges related to the delays in seeking and gaining access to cardiovascular treatments caused by the COVID-19 pandemic and the impact this will have upon waiting lists.Background: cardiovascular disease is the leading cause of death for men in the UK and second-most for women. During the first lockdown from March 2020, elective cardiac procedures and many outpatient consultations were postponed and a substantial number of appointments have not yet been rescheduled. In addition, those who were suffering from heart conditions did not present to their GP or hospital – either because they did not want to impact further on NHS resources, or through concern of being exposed to the virus. Clinicians have been able to report what has been happening with respect to the reduction in emergency cardiac admissions and procedures, as well as quantify the excess deaths from emergency cardiac conditions. They have not quantified the impact on outpatient consultations.It would be helpful to form a predictive model of the outcome of different strategies for recovery of the backlog in cardiac procedures and outpatient consultations, noting that a number of competing elements are at play including incident cases, prevalent cases, delayed cases, abandonment from changes in disease and deaths, as well as the capacity and capability of NHS services to respond. For example, given different strategies for recovery from this major perturbation to treatment, what would be the implications for treatment demand over timescales from say 6 months to several years? How should treatment be optimised given resource constraints? What would be the impact of additional waves of COVID-19 cases?Aims and objectives: this study group brought together researchers and clinicians to provide further insight into these complex challenges through a variety of mathematical approaches.Proposed issues explored related to:1. The overarching state of the delivery of elective cardiovascular procedures and outpatient consultations at the national level, as a result of the pandemic and how this plays out at regional or local (single NHS trust) levels.2. An exemplar procedure - Aortic Stenosis – for which there is a particularly well-defined data set and for which missed early intervention can lead to particularly adverse outcomes over the course of one or two years.3. An exemplar condition – chronic heart failure - treatment regimens for which are less well-defined, yet the missed appointments during the pandemic represent a major perturbation to care that may impact on the optimal management of resources within cardiology departments.These were discussed in light of the following concerns:Where people are not presenting to clinics now, what will the impact of this be further down the line, as their health issue has not gone away? If people don’t present for treatment but don’t die, what impact does that have on resources?What could the knock-on effect of additional lockdowns be?If and when hospitals return to normal, what would be the optimal way to recover from the backlog and avoid a situation where more urgent cases in poorer condition are prioritised over routine earlier interventions, leading to perpetual worse outcomes for everyone.How can we configure a decision support system that could enable day-to-day answers to these questions on the ground?Previous work through V-KEMS discussed general mathematical principles which was considered and a number of different scenarios were modelled.Following the event, Plus Magazine interviewed Dr Jess Enright (University of Glasgow) and Dr Ramesh Nadarajah (University of Leeds). Ramesh presented the challenges at the event and Jess was one of the modelers who helped to develop the event. The inspiring podcast can be heard here. A working paper is published below, which highlights the discussions that took place at the Study Group and the initial findings.<br/
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