Perceived Stress Scale: Reliability and Validity Study in Greece

Objective: To translate the Perceived Stress Scale (versions PSS-4, -10 and -14) and to assess its psychometric properties in a sample of general Greek population. Methods: 941 individuals completed anonymously questionnaires comprising of PSS, the Depression Anxiety and Stress scale (DASS-21 version), and a list of stress-related symptoms. Psychometric properties of PSS were investigated by confirmatory factor analysis (construct validity), Cronbach’s alpha (reliability), and by investigating relations with the DASS-21 scores and the number of symptoms, across individuals’ characteristics. The two-factor structure of PSS-10 and PSS-14 was confirmed in our analysis. We found satisfactory Cronbach’s alpha values (0.82 for the full scale) for PSS-14 and PSS-10 and marginal satisfactory values for PSS-4 (0.69). PSS score exhibited high correlation coefficients with DASS-21 subscales scores, meaning stress (r = 0.64), depression (r = 0.61), and anxiety (r = 0.54). Women reported significantly more stress compared to men and divorced or widows compared to married or singled only. A strong significant (p < 0.001) positive correlation between the stress score and the number of self-reported symptoms was also noted. Conclusions: The Greek versions of the PSS-14 and PSS-10 exhibited satisfactory psychometric properties and their use for research and health care practice is warranted

The SIPHER consortium : introducing the new UK hub for systems science in public health and health economic research

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER (Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium’s vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing

Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios

The Adaptive Robust Design Approach: Improving Analytical Support under Deep Uncertainty

Policymaking often involves different parties such as policymakers, stakeholders, and analysts each with distinct roles in the process. To assist policymakers, policy analysts help in structuring the problem, designing, and evaluating policy alternatives. Analysts face many challenges, like complexity and uncertainty in a system of interest, while supporting the policymaking process. Frequently, analysts rely on mathematical models that represent the key features of the system. Assumptions made during modelling introduce a significant level of uncertainty in the models, and forecasting based on models is therefore always bound by this uncertainty. Instead of focusing on limited best-estimate predictions under uncertainty, exploring a plethora of plausible futures by using mathematical models can help supporting decision-making. In current practice, uncertainty analysis for decision-making is mostly limited to technical and shallow uncertainties but not focused on deep uncertainty. This thesis contributes to a solution for enhanced handling of deep uncertainty to support policymaking. We have developed a new methodological approach for improving analytical support for policymaking under deep uncertainty, and demonstrated each analytical advancement stage with case studies. This thesis proposes to improve analytical support for policymaking to better handle deep uncertainty. Building upon the existing pragmatic practice, a systematic approach for designing adaptive policies under uncertainty is developed. The Adaptive Robust Design (ARD) approach in combination with multi-objective robust optimization will improve the support for policymaking under deep uncertainty. The effectiveness of ARD for developing adaptive robust policies under deep uncertainty is shown by illustrative case studies.Policy Analysi

Exploring the Future of Wind-Powered Energy

Although there is a trend towards more sustainable energy system, the future of renewable energies is still deeply uncertain. Among the renewable resources, wind energy is considered to be a promising one. However, in the presence of deep uncertainty, what will be the future of wind-powered energy? Decision making under deep uncertainty for such issues requires an explorative manner. Since predictions under deep uncertainty can be extremely misleading, exploration of plausible futures should be the main approach. In this paper, a new research methodology, Exploratory Modeling and Analysis (EMA), to deal with deep uncertainty will be presented. Three System Dynamics models about Wind-powered energy will be explored using EMA and results of possible policy implementations will be illustrated.Multi Actor SystemsTechnology, Policy and Managemen

The Influenza A(H1N1)v Pandemic: An Exploratory System Dynamics Approach

This paper presents a small exploratory System Dynamics model related to the dynamics of the 2009 flu pandemic, also known as the Mexican flu, swine flu, or A(H1N1)v. The model was developed in May 2009 in order to quickly foster understanding about the possible dynamics of this new flu variant and to perform rough-cut policy explorations. Later, the model was also used to further develop and illustrate the use of Exploratory System Dynamics models as scenario generators for Exploratory Modelling and Analysis. The paper starts with an introduction to, and a description of, the exploratory System Dynamics model, followed by a discussion of plausible behaviours, sensitivity, what-if and policy analyses. The model is subsequently used to illustrate the Exploratory System Dynamics Modelling and Analysis approach: base case behaviours are discussed, followed by sensitivity, what-if and policy analyses. Finally some concluding remarks and policy recommendations are formulated.Multi Actor SystemsTechnology, Policy and Managemen

A decision support system framework for innovation management

In this paper, we present a Decision Support System (DSS) framework for innovation management. The proposed framework utilizes the data acquired by a web based questionnaire which is filled in by the upper managers of the manufacturing companies. The DSS compares their answers with the responds of the other companies existing in the database of the DSS and determines the weak points of the company which have room for further improvement and at the same time significantly affect innovativeness. Finally, the DSS automatically generates two reports. The first report benchmarks the company with the others and the second report suggests policies that might be helpful to improve the innovativeness of the company

The Concerted Run on the DSB Bank: An Exploratory System Dynamics Approach

In this paper, an Exploratory System Dynamics model of a concerted run is first of all presented. The immediate cause for modelling a concerted bank run was the mediatised call for a run on the DSB bank. This Exploratory System Dynamics model was developed the morning of the call for the bank run, before the start of the ensuing bank crisis, in order to quickly foster understanding of possible dynamic behaviours of ‘concerted’ bank runs and to perform rough-cut policy/strategy analyses. The model is subsequently used to illustrate the combination of Exploratory System Dynamics modelling and Exploratory Modelling and Analysis, or Exploratory System Dynamics Modelling and Analysis. The paper starts with a short overview of the DSB Bank crisis, the description of the exploratory System Dynamics model and some quick exploratory analyses. The model is then used as a scenario generator for Exploratory System Dynamics Modelling and Analysis in order to analyse and deal with deep uncertainties surrounding the issue and its modelling (parameters and functions). The paper ends with some applied conclusions and policy recommendations, methodological conclusions, and venues for future work.Multi Actor SystemsTechnology, Policy and Managemen