The COVID-19 vaccine communication handbook. A practical guide for improving vaccine communication and fighting misinformation

This handbook is for journalists, doctors, nurses, policy makers, researchers, teachers, students, parents – in short, it’s for everyone who wants to know more: About the COVID-19 vaccines; How to talk to others about them; How to challenge misinformation about the vaccines.Published versio

Human Dimensions of the Ecosystem Approach to Fisheries: An Overview of Context, Concepts, Tools and Methods

This document aims to provide a better understanding of the role of the economic, institutional and sociocultural components within the ecosystem approach to fisheries (EAF) process and to examine some potential methods and approaches that may facilitate the adoption of EAF management. It explores both the human context for the ecosystem approach to fisheries and the human dimensions involved in implementing the EAF. For the former, the report provides background material essential to understand prior to embarking on EAF initiatives, including an understanding of key concepts and issues, of the valuation of aquatic ecosystems socially, culturally and economically, and of the many policy, legal, institutional, social and economic considerations relevant to the EAF. With respect to facilitating EAF implementation, the report deals with a series of specific aspects: (1) determining the boundaries, scale and scope of the EAF; (2) assessing the various benefits and costs involved, seen from social, economic, ecological and management perspectives; (3) utilizing appropriate decision-making tools in EAF; (4) creating and/or adopting internal incentives and institutional arrangements to promote, facilitate and fund the adoption of EAF management; and (5) finding suitable external (non-fisheries) approaches for financing EAF implementation

Building Bayesian Networks: Elicitation, Evaluation, and Learning

As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesian networks are widely used for efficient reasoning underuncertainty in a variety of applications, from medical diagnosis to computertroubleshooting and airplane fault isolation. However, construction of Bayesiannetworks is often considered the main difficulty when applying this frameworkto real-world problems. In real world domains, Bayesian networks are often built by knowledge engineering approach. Unfortunately, eliciting knowledge from domain experts isa very time-consuming process, and could result in poor-quality graphicalmodels when not performed carefully. Over the last decade, the research focusis shifting more towards learning Bayesian networks from data, especially withincreasing volumes of data available in various applications, such asbiomedical, internet, and e-business, among others.Aiming at solving the bottle-neck problem of building Bayesian network models, thisresearch work focuses on elicitation, evaluation and learning Bayesiannetworks. Specifically, the contribution of this dissertation involves the research in the following five areas:a) graphical user interface tools forefficient elicitation and navigation of probability distributions, b) systematic and objective evaluation of elicitation schemes for probabilistic models, c)valid evaluation of performance robustness, i.e., sensitivity, of Bayesian networks,d) the sensitivity inequivalent characteristic of Markov equivalent networks, and the appropriateness of using sensitivity for model selection in learning Bayesian networks,e) selective refinement for learning probability parameters of Bayesian networks from limited data with availability of expert knowledge. In addition, an efficient algorithm for fast sensitivity analysis is developed based on relevance reasoning technique. The implemented algorithm runs very fast and makes d) and e) more affordable for real domain practice

Researcher’s Reactions to Compelled Disclosure of Scientific Information

Demands placed on researchers by subpoenas for scientific information are not necessarily any greater than those placed on other third-party recipients of subpoenas

Developing an Intervention Toolbox for the Common Health Problems in the Workplace

Development of the Health ↔ Work Toolbox is described. The toolbox aims to reduce the workplace impact of common health problems (musculoskeletal, mental health, and stress complaints) by focusing on tackling work-relevant symptoms. Based on biopsychosocial principles this toolbox supplements current approaches by occupying the zone between primary prevention and healthcare. It provides a set of evidence-informed principles and processes (knowledge + tools) for tackling work-relevant common health problems. The toolbox comprises a proactive element aimed at empowering line managers to create good jobs, and a ‘just in time’ responsive element for supporting individuals struggling with a work-relevant health problem. The key intention is helping people with common health problems to maintain work participation. The extensive conceptual and practical development process, including a comprehensive evidence review, produced a functional prototype toolbox that is evidence based and flexible in its use. End-user feedback was mostly positive. Moving the prototype to a fully-fledged internet resource requires specialist design expertise. The Health ↔ Work Toolbox appears to have potential to contribute to the goal of augmenting existing primary prevention strategies and healthcare delivery by providing a more comprehensive workplace approach to constraining sickness absence

Participatory methods, guidelines and good practice guidance to be applied throughout the project to enhance problem definition, co-learning, synthesis and dissemination

Research Methods/ Statistical Methods,