The importance of a sustainable health workforce is increasingly recognised. However, the building of a future health workforce that is responsive to diverse population needs and demographic and economic change remains insufficiently understood. There is a compelling argument to be made for a comprehensive research agenda to address the questions. With a focus on Europe and taking a health systems approach, we introduce an agenda linked to the ‘Health Workforce Research’ section of the European Public Health Association. Six major objectives for health workforce policy were identified: (1) to develop frameworks that align health systems/governance and health workforce policy/planning, (2) to explore the effects of changing skill mixes and competencies across sectors and occupational groups, (3) to map how education and health workforce governance can be better integrated, (4) to analyse the impact of health workforce mobility on health systems, (5) to optimise the use of international/EU, national and regional health workforce data and monitoring and (6) to build capacity for policy implementation. This article highlights critical knowledge gaps that currently hamper the opportunities of effectively responding to these challenges and advising policy-makers in different health systems. Closing these knowledge gaps is therefore an important step towards future health workforce governance and policy implementation. There is an urgent need for building health workforce research as an independent, interdisciplinary and multi-professional field. This requires dedicated research funding, new academic education programmes, comparative methodology and knowledge transfer and leadership that can help countries to build a people-centred health workforce.
Introduction:Integrated care programmes are increasingly being put in place to provide care to older people who live at home. Knowledge of how to further develop integrated care and how to transfer successful initiatives to other contexts is still limited. Therefore, a cross-European research project, called Sustainable Tailored Integrated Care for Older People in Europe (SUSTAIN), has been initiated with a twofold objective: 1. to collaborate with local stakeholders to support and monitor improvements to established integrated care initiatives for older people with multiple health and social care needs. Improvements focus on person-centredness, prevention orientation, safety and efficiency; 2. to make these improvements applicable and adaptable to other health and social care systems, and regions in Europe. This paper presents the overall structure and approach of the SUSTAIN project.Methods:SUSTAIN uses a multiple embedded case study design. In three phases, SUSTAIN partners: (i) conduct interviews and workshops with stakeholders from fourteen established integrated care initiatives to understand where they would prefer improvements to existing ways of working; (ii) collaborate with local stakeholders to support the design and implementation of improvement plans, evaluate implementation progress and outcomes per initiative, and carry out overarching analyses to compare the different initiatives, and; (iii) translate knowledge and experience to an online roadmap.Discussion:SUSTAIN aims to generate evidence on how to improve integrated care, and apply and transfer the knowledge gained to other health and social care systems, and regions. Lessons learned will be brought together in practical tools to inform and support policy-makers and decision-makers, as well as other stakeholders involved in integrated care, to manage and improve care for older people living at home.
Electroencephalogram (EEG) signal classification plays an important role to facilitate physically impaired patients by providing brain-computer interface (BCI)-controlled devices. However, practical applications of BCI make it difficult to decode motor imagery-based brain signals for multiclass classification due to their non-stationary nature. In this study, we aim to improve multiclass classification accuracy for motor imagery movement using sub-band common spatial patterns with sequential feature selection (SBCSP-SBFS) method. Filter bank having bandpass filters of different overlapped frequency cutoffs is applied to suppress the noise signals from raw EEG signals. The output of these sub-band filters is sent for feature extraction by applying common spatial pattern (CSP) and linear discriminant analysis (LDA). As all of the extracted features are not necessary for classification therefore, selection of optimal features is done by passing the extracted features to sequential backward floating selection (SBFS) technique. Three different classifiers were then trained on these optimal features, i.e., support vector machine (SVM), Naïve-Bayesian Parzen-Window (NBPW), and k-Nearest Neighbor (KNN). Results are evaluated on two datasets, i.e., Emotiv Epoc and wet gel electrodes for three classes, i.e., right-hand motor imagery, left hand motor imagery, and rest state. The proposed model yields a maximum accuracy of 60.61% in case of Emotiv Epoc headset and 86.50% for wet gel electrodes. The computed accuracy shows an increase of 7% as compared to previously implemented multiclass EEG classification.
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