AimTo evaluate healthcare professionals' performance and treatment fidelity in the Cardiac Care Bridge (CCB) nurse‐coordinated transitional care intervention in older cardiac patients to understand and interpret the study results.DesignA mixed‐methods process evaluation based on the Medical Research Council Process Evaluation framework.MethodsQuantitative data on intervention key elements were collected from 153 logbooks of all intervention patients. Qualitative data were collected using semi‐structured interviews with 19 CCB professionals (cardiac nurses, community nurses and primary care physical therapists), from June 2017 until October 2018. Qualitative data‐analysis is based on thematic analysis and integrated with quantitative key element outcomes. The analysis was blinded to trial outcomes. Fidelity was defined as the level of intervention adherence.ResultsThe overall intervention fidelity was 67%, ranging from severely low fidelity in the consultation of in‐hospital geriatric teams (17%) to maximum fidelity in the comprehensive geriatric assessment (100%). Main themes of influence in the intervention performance that emerged from the interviews are interdisciplinary collaboration, organizational preconditions, confidence in the programme, time management and patient characteristics. In addition to practical issues, the patient's frailty status and limited motivation were barriers to the intervention.ConclusionAlthough involved healthcare professionals expressed their confidence in the intervention, the fidelity rate was suboptimal. This could have influenced the non‐significant effect of the CCB intervention on the primary composite outcome of readmission and mortality 6 months after randomization. Feasibility of intervention key elements should be reconsidered in relation to experienced barriers and the population.ImpactIn addition to insight in effectiveness, insight in intervention fidelity and performance is necessary to understand the mechanism of impact. This study demonstrates that the suboptimal fidelity was subject to a complex interplay of organizational, professionals' and patients' issues. The results support intervention redesign and inform future development of transitional care interventions in older cardiac patients.
MULTIFILE
Introduction: In March 2014, the New South Wales (NSW) Government (Australia) announced the NSW Integrated Care Strategy. In response, a family-centred, population-based, integrated care initiative for vulnerable families and their children in Sydney, Australia was developed. The initiative was called Healthy Homes and Neighbourhoods. A realist translational social epidemiology programme of research and collaborative design is at the foundation of its evaluation. Theory and Method: The UK Medical Research Council (MRC) Framework for evaluating complex health interventions was adapted. This has four components, namely 1) development, 2) feasibility/piloting, 3) evaluation and 4) implementation. We adapted the Framework to include: critical realist, theory driven, and continuous improvement approaches. The modified Framework underpins this research and evaluation protocol for Healthy Homes and Neighbourhoods. Discussion: The NSW Health Monitoring and Evaluation Framework did not make provisions for assessment of the programme layers of context, or the effect of programme mechanism at each level. We therefore developed a multilevel approach that uses mixed-method research to examine not only outcomes, but also what is working for whom and why.
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In de openbare geestelijke gezondheidszorg is bemoeizorg al een tijdje bekend. Hulpverleners proberen daarbij in contact te komen met ‘zorgwekkende zorgmijders’; een risicogroep van mensen met vaak complexe en meervoudige problematiek die zelf niet om hulp vragen.
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Coastal nourishments, where sand from offshore is placed near or at the beach, are nowadays a key coastal protection method for narrow beaches and hinterlands worldwide. Recent sea level rise projections and the increasing involvement of multiple stakeholders in adaptation strategies have resulted in a desire for nourishment solutions that fit a larger geographical scale (O 10 km) and a longer time horizon (O decades). Dutch frontrunner pilot experiments such as the Sandmotor and Ameland inlet nourishment, as well as the Hondsbossche Dunes coastal reinforcement project have all been implemented from this perspective, with the specific aim to encompass solutions that fit in a renewed climate-resilient coastal protection strategy. By capitalizing on recent large-scale nourishments, the proposed Coastal landSCAPE project C-SCAPE will employ and advance the newly developed Dynamic Adaptive Policy Pathways (DAPP) approach to construct a sustainable long-term nourishment strategy in the face of an uncertain future, linking climate and landscape scales to benefits for nature and society. Novel long-term sandy solutions will be examined using this pathways method, identifying tipping points that may exist if distinct strategies are being continued. Crucial elements for the construction of adaptive pathways are 1) a clear view on the long-term feasibility of different nourishment alternatives, and 2) solid, science-based quantification methods for integral evaluation of the social, economic, morphological and ecological outcomes of various pathways. As currently both elements are lacking, we propose to erect a Living Lab for Climate Adaptation within the C-SCAPE project. In this Living Lab, specific attention is paid to the socio-economic implications of the nourished landscape, as we examine how morphological and ecological development of the large-scale nourishment strategies and their design choices (e.g. concentrated vs alongshore uniform, subaqueous vs subaerial, geomorphological features like artificial lagoons) translate to social acceptance.
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.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
