Background: Persons with an intellectual disability are at increased risk of experiencing adversities. The current study aims at providing an overview of the research on how resilience in adults with intellectual disabilities, in the face of adversity, is supported by sources in their social network. Method: A literature review was conducted in the databases Psycinfo and Web of Science. To evaluate the quality of the included studies, the Mixed Method Appraisal Tool (MMAT) was used. Results: The themes: “positive emotions,” “network acceptance,” “sense of coherence” and “network support,” were identified as sources of resilience in the social network of the adults with intellectual disabilities. Conclusion: The current review showed that research addressing sources of resilience among persons with intellectual disabilities is scarce. In this first overview, four sources of resilience in the social network of people with intellectual disabilities were identified that interact and possibly strengthen each other.
BACKGROUND: Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them.METHODS: Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined.RESULTS: We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small.CONCLUSIONS: We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.
Abstract: Electronic and electrical waste (e-waste) is growing fast. The purpose of this study is to examine young consumers’ purchase intention of refurbished electronic devices (REDs) such as laptop, tablet, mobile phone and game console. From literature review the factors that influence young consumers’ purchase intention were identified as ‘environmental awareness’, ‘social acceptance’, ‘seller/brand reputation and availability’, and ‘affordability and value’. For each factor a few statements were developed and used as independent variables in a questionnaire. One statement was added about purchase intention as dependent variable. A Pearson correlation coefficient test us showed a clear positive correlation of ‘environmental awareness’ and ‘affordability and value’ with the intention to purchase REDs, but not for the other two factors. This analysis contributes to knowledge on young consumers’ perceptions of refurbished electronic devices and can inform the design of innovative value propositions and new business models for REDs that contribute to a circular economy
MULTIFILE
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Social enterprises (SEs) can play an important role in addressing societal problems. SEs are businesses whose primary objective is to generate social impact (e.g. well-being, social wealth and cohesion, and ecology) through a market-based model. SEs achieve this through a hybrid business model, trading-off financial and social value creation objectives. SEs typically face higher costs, for example because of ethical sourcing principles and/or production processes centering around the needs of workers who are vulnerable or hard-to-employ. This results in SEs’ struggling to scale-up due to their relatively costly operating model. Traditional management techniques are not always appropriate, as they do not take into account the tensions between financial and social value creation objectives of SEs. Our project examines how continuous improvement, and in particular the philosophy and tools of Lean can be harnessed to improve SEs competitiveness. Lean organizations share many values with SEs, such as respect for people, suggesting a good fit between the values and principles of Lean and those of SEs. The consortium for this project is a cooperation between the research groups Improving Business and New Marketing of the Center of Expertise Well-Being Economy and New Entrepreneurship and the minor Continuous Improvement of AVANS Hogeschool, and the SME companies Elliz in Company and Ons Label. The project consists of two phases, an exploratory phase during which the question “in what ways can the philosophy and tools of Lean be used by Social Enterprises?” will be addressed. Interviews and focus groups will be conducted with multiple SEs (not only partners). Participant observation will be conducted by the students of the minor Continuous Improvement at the partner SEs. During the second phase, the implementation of the identified principles and tools will be operationalized through a roadmap. Action research will be conducted in cooperation with the partner SEs.
