Assessing the quality of regional climate information

There are now a plethora of data, models, and approaches available to produce regional and local climate information intended to inform adaptation to a changing climate. There is, however, no framework to assess the quality of these data, models, and approaches that takes into account the issues that arise when this information is produced. An evaluation of the quality of regional climate information is a fundamental requirement for its appropriate application in societal decision-making. Here, an analytical framework is constructed for the quality assessment of science-based statements and estimates about future climate. This framework targets statements that project local and regional climate at decadal and longer time scales. After identifying the main issues with evaluating and presenting regional climate information, it is argued that it is helpful to consider the quality of statements about future climate in terms of 1) the type of evidence and 2) the relationship between the evidence and the statement. This distinction not only provides a more targeted framework for quality, but also shows how certain evidential standards can change as a function of the statement under consideration. The key dimensions to assess regional climate information quality are diversity, completeness, theory, adequacy for purpose, and transparency. This framework is exemplified using two research papers that provide regional climate information and the implications of the framework are explored

Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals

Developing a framework to facilitate the assessment of asset management information quality in facilities management operations

Information quality issues have taken an increased importance in academia and industry and have many causes attributed to it. Poor information quality presents significant costs to an organisation, financially and non-financially. However, the issue of information quality has not been explored in depth in the field of facilities management. Thus, within the context of facilities management, this has led to failures in asset management programs undertaken by facilities management organisations and present a significant challenge in the decision-making process by facility managers. Achieving improved asset information quality in facility management operations is thus of immense importance to stakeholders in facilities management. To this end, various methods and frameworks have been developed to assess and improve information quality, but these have very limited scope and not applicable in facilities management organisation undertaking asset management. To this end, this research aims to evaluate the quality of information and determine what factors impact information quality of asset management programs in the facilities management domain. The research adopts an exploratory mixed-method methodology, which allows data to be collected and analysed using qualitative and quantitative techniques to provide greater insight of the phenomenon in facility management operations. The qualitative approach uses thematic analysis to determine what factors affect the quality of information from the information, organisation, and people domain respectively. The results from this analysis show that factors affecting information quality of asset management programs is multidimensional. In addition, 71 information quality attributes has been identified from the qualitative analysis. In the quantitative phase, principal component analysis (PCA) with direct oblimin rotation, ANOVA, and measure of central tendency (MCT) techniques were adopted to identify the specific dimensions of information quality in asset management, the effect of the structure of the organisation on information quality, and the prevalence of information quality issue in asset management respectively. The results from the analysis identified 12 information quality dimensions which was grouped into seven (7) categories. Also a high prevalence of information quality issue experienced by facilities management professionals was observed with a mean value of 7.90 and median value of 8.00 and standard deviation of 1.467 which was normally distributed. Further analysis indicated that the hierarchical structure of the organisation had an effect on information quality which was statistically significant. Based on the result of the analysis, a framework, using a perceptual map premised on multi-dimensional scale (MDS) technique, has been developed that seeks to assess the quality of information of asset management programs in facilities management

The Vulnverability Cube: A Multi-Dimensional Framework for Assessing Relative Vulnerability

The diversity and abundance of information available for vulnerability assessments can present a challenge to decision-makers. Here we propose a framework to aggregate and present socioeconomic and environmental data in a visual vulnerability assessment that will help prioritize management options for communities vulnerable to environmental change. Socioeconomic and environmental data are aggregated into distinct categorical indices across three dimensions and arranged in a cube, so that individual communities can be plotted in a three-dimensional space to assess the type and relative magnitude of the communities’ vulnerabilities based on their position in the cube. We present an example assessment using a subset of the USEPA National Estuary Program (NEP) estuaries: coastal communities vulnerable to the effects of environmental change on ecosystem health and water quality. Using three categorical indices created from a pool of publicly available data (socioeconomic index, land use index, estuary condition index), the estuaries were ranked based on their normalized averaged scores and then plotted along the three axes to form a vulnerability cube. The position of each community within the three-dimensional space communicates both the types of vulnerability endemic to each estuary and allows for the clustering of estuaries with like-vulnerabilities to be classified into typologies. The typologies highlight specific vulnerability descriptions that may be helpful in creating specific management strategies. The data used to create the categorical indices are flexible depending on the goals of the decision makers, as different data should be chosen based on availability or importance to the system. Therefore, the analysis can be tailored to specific types of communities, allowing a data rich process to inform decision-making

Operational excellence assessment framework for manufacturing companies

Operational Excellence (OE) is a consequence of an enterprise-wide practises based on correct principles that can be classified under four dimensions; Culture, Continuous Process Improvement, Enterprise Alignment and Results. To achieve OE, organisations have to attain a high maturity level and measurable success in the four dimensions as assessed externally by accredited institutions or consultants. External assessment is costly and can be inaccurate due to the lack of in depth knowledge of the organisation by external assessors, on the contrary, self-assessment of an organisations OE is cost effective and accurate if performed with a complete tool which assesses all four dimensions of OE. A complete OE self-assessment tool is currently unavailable, thus this study focuses on the development of a complete OE self-assessment tool. Using a matrix to critically evaluate and compare existing self-assessment tools in areas such as dimensions assessed, scoring criteria and usability, a complete self-assessment tool is then developed based on the combination of existing assessment tools. The tool is validated through the application, by managers, within a manufacturing company that already implements aspects of lean in order to self-assess its OE. The results of the assessment form the basis on which a roadmap to achieving OE is then developed

What makes an expert persuasive? Examining the influence of evidence quality and superficial cues on jurors' evaluation of expert evidence

Expert evidence offers important aid in legal trials, and often lay jurors must decide how much weight the expert opinion ought to be given when making consequential decisions (e.g., whether a defendant is implicated in a crime). However, there is question about lay jurors’ ability to assess the claims of experts who usually present scientific, complex, and technical evidence, some of which might be credible and some of which might not. There is concern that jurors might show limited sensitivity to expert evidence quality, and that they may be influenced by superficial factors unrelated to evidence quality (i.e., expert likeability, or gender). However, research to-date that has underpinned these concerns has primarily employed limited manipulations of expert evidence quality, limited manipulation of superficial cues and/or has confounded these two dimensions of expert opinion. Thus, our understanding of how jurors examine evidence provided by experts in the presence of relevant and irrelevant information remains incomplete. In this thesis, across eight experiments using jury-eligible respondents (N = 2255), we explored jurors’ evaluation of expert evidence and examined 1) whether jurors are persuaded by higher quality expert evidence relative to lower quality expert evidence 2) whether jurors are influenced by superficial cues and 3) whether evidence quality and superficial cues interact to influence juror evaluations. We used the Expert Persuasion Expectancy (ExPEx) framework (Martire et al., 2020) to holistically operationalise higher and lower quality expert evidence. In relation to superficial cues, we explored the influence of four salient superficial cues– attractiveness, likeability, gender, and confidence. As such, we present evidence that provides a renewed understanding of expert persuasion, credibility assessment and jurors’ decision-making. Overall, we found that when jurors were provided with rich information about expert evidence and its underlying quality, they were consistently more persuaded by higher quality expert evidence than lower quality expert evidence. Further, we found mixed effects of superficial cues. Specifically, we found significant effects of likeability and confidence on persuasiveness, but the impact of these attributes was in addition to evidence quality, as opposed to instead of evidence quality. Further, we found little evidence to suggest an interactive effect of evidence quality and superficial cues. We also found that it was mostly subjective perceptions of expert evidence quality features that influenced jurors’ decision-making (i.e., verdict or sentencing decision). Together, these findings suggest that in information-rich environments, jurors can be sensitive to evidence quality, and that while some superficial cues can influence judgements, they do so in addition to evidence quality, as opposed to in place of

Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making

Mapping Varieties of Industrial Relations: Eurofound\u27s Analytical Framework Applied

Eurofound’s 2016 report Mapping key dimensions of industrial relations identified four key dimensions of industrial relations: industrial democracy, industrial competitiveness, social justice, and quality of work and employment. This report builds upon that earlier study, developing a dashboard of 45 indicators to assess how and to what extent the conceptual framework of these key dimensions can be applied at national level. The indicators were tested across the Member States by Eurofound’s Network of European Correspondents and show reasonable accuracy when used to map the predominant features and trends of the national industrial relations systems. The study confirms that a dashboard of indicators that can accurately measure and summarise the complex reality of industrial relations across the EU is a valuable tool for comparative research and a useful instrument for supporting policymakers, social partners and stakeholders. The report sets out a range of options for further developing this conceptual approach

The elicitation of key performance indicators of e-government providers: A bottom-up approach

Copyright @ 2013 EMCIS.Delivering an adequate e-Government service (e-service) is becoming more of a necessity in today's digital world. In order to improve e-services and increase the engagement of both users' and providers' side, studies on the performance evaluation of such provided e-services are taking places. However a clear identification of the key performance indicators from the e-Government providers’ side is not well explored. This shortcoming hampers the conduct of a holistic evaluation of an e-service provision from the perspective of its stakeholders in order to improve e-services as well as to increase e-services take-ups. In this paper, a systematic process to identify indicators is implemented based on a bottom-up approach. The process used three focus-group meetings with providers, users, and academics in Qatar, Lebanon and UK to collect, identify and validate key indicators from the perspective of e-services’ providers. The approach resulted in the identification of five factors levels (service, technology, employees, policy and management and social responsibilities) with fifteen sub-categories of SMART variables. Hence, leading to the development of a new model, STEPS, that can fully explain and predict e-government success from the providers’ point of view. It will work as a strategic management tool to align various stakeholders on common goal and values based on evidence based evaluation of e-services using smart measurable indicators for the improvement of an e-service at the engagement level in the field of e-government. In addition, other fields can benefit from the outcome of this work, such as logistics service providers, who make their services available across new and existing relationships between the Internet commerce firms, their customers, and their vendors